if(!require(correlation)){install.packages("correlation"); library(correlation)}
## Loading required package: correlation
if(!require(car)){install.packages("car"); library(car)}
## Loading required package: car
## Loading required package: carData
if(!require(mgcv)){install.packages("mgcv"); library(mgcv)}
## Loading required package: mgcv
## Loading required package: nlme
## This is mgcv 1.9-0. For overview type 'help("mgcv-package")'.
if(!require(rpart)){install.packages("rpart"); library(rpart)}
## Loading required package: rpart
if(!require(ggplot2)){install.packages("ggplot2"); library(ggplot2)}
## Loading required package: ggplot2
if(!require(gridExtra)){install.packages("gridExtra"); library(gridExtra)}
## Loading required package: gridExtra
if(!require(lme4)){install.packages("lme4"); library(lme4)}
## Loading required package: lme4
## Loading required package: Matrix
## 
## Attaching package: 'lme4'
## The following object is masked from 'package:nlme':
## 
##     lmList
if(!require(Matrix)){install.packages("Matrix"); library(Matrix)}
if(!require(lmtest)){install.packages("lmtest"); library(lmtest)}
## Loading required package: lmtest
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
if(!require(gamm4)){install.packages("gamm4"); library(gamm4)}
## Loading required package: gamm4
## This is gamm4 0.2-6
if(!require(sjPlot)){install.packages("sjPlot"); library(sjPlot)}
## Loading required package: sjPlot
## #refugeeswelcome
if(!require(sjmisc)){install.packages("sjmisc"); library(sjmisc)}
## Loading required package: sjmisc
if(!require(sjlabelled)){install.packages("sjlabelled"); library(sjlabelled)}
## Loading required package: sjlabelled
## 
## Attaching package: 'sjlabelled'
## The following object is masked from 'package:ggplot2':
## 
##     as_label
if(!require(performance)){install.packages("performance"); library(performance)}
## Loading required package: performance
if(!require(glmmTMB)){install.packages("glmmTMB"); library(glmmTMB)}
## Loading required package: glmmTMB
## Warning in checkDepPackageVersion(dep_pkg = "TMB"): Package version inconsistency detected.
## glmmTMB was built with TMB version 1.9.10
## Current TMB version is 1.9.11
## Please re-install glmmTMB from source or restore original 'TMB' package (see '?reinstalling' for more information)
if(!require(DHARMa)){install.packages("DHARMa"); library(DHARMa)}
## Loading required package: DHARMa
## This is DHARMa 0.4.6. For overview type '?DHARMa'. For recent changes, type news(package = 'DHARMa')

Import Dataset

#Import data
week_kuds <- read.csv("week_kuds2.2.csv", sep=";") #has the wrong week values
week_kuds3 <- read.csv("week_kuds1 - usar.csv", sep=";") #has the right week values
week_kuds$Week <- week_kuds3$Week #substitute the wrong for the right week in the dataset
#create week variable without the year
names(week_kuds)[names(week_kuds) == "Week"] <- "WeekYear"
week_kuds$Week <- substr(week_kuds$WeekYear, 1, 2)
#create year variable without the week
week_kuds$Year <- substr(week_kuds$WeekYear, 4, 7)

week_kuds$File <- as.factor(week_kuds$File)
week_kuds$Species <- as.factor(week_kuds$Species)
week_kuds$Transmitter <- as.factor(week_kuds$Transmitter)
week_kuds$KUD50 <- as.numeric(week_kuds$KUD50)
week_kuds$KUD95 <- as.numeric(week_kuds$KUD95)
week_kuds$Habitat <- as.factor(week_kuds$Habitat)
week_kuds$Migration <- as.factor(week_kuds$Migration)
week_kuds$ComImport <- as.factor(week_kuds$ComImport)
week_kuds$Length_cm <- as.numeric(week_kuds$Length_cm)
week_kuds$LengthStd <- as.numeric(week_kuds$LengthStd)
week_kuds$BodyMass <- as.numeric(week_kuds$BodyMass)
week_kuds$BodyMassStd <- as.numeric(week_kuds$BodyMassStd)
week_kuds$Longevity <- as.numeric(week_kuds$Longevity)
week_kuds$Vulnerability <- as.numeric(week_kuds$Vulnerability)
week_kuds$Troph <- as.numeric(week_kuds$Troph)
week_kuds$ReceiverDensity <- as.numeric(week_kuds$ReceiverDensity)
week_kuds$MonitArea_km2 <- as.numeric(week_kuds$MonitArea_km2)
week_kuds$MCP_km2 <- as.numeric(week_kuds$MCP_km2)
week_kuds$NReceivers <- as.numeric(week_kuds$NReceivers)
week_kuds$MaxDistReceivers <- as.numeric(week_kuds$MaxDistReceivers)
week_kuds$MaxLength <- as.numeric(week_kuds$MaxLength)
week_kuds$MaxBodyMass <- as.numeric(week_kuds$MaxBodyMass)
week_kuds$a <- as.numeric(week_kuds$a)
week_kuds$b <- as.numeric(week_kuds$b)
week_kuds$Week <- as.factor(week_kuds$Week)
week_kuds$Year <- as.factor(week_kuds$Year)
week_kuds$Spawn <- as.factor(week_kuds$Spawn)

week_kuds$Spawn <- with(week_kuds, ifelse((SpawnSeason == "SS" & Week %in% c("11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40")) |
                          (SpawnSeason == "A" & Week %in% c("41", "42", "43", "44", "45", "46", "47", "48", "49", "50")) |
                          (SpawnSeason == "W" & Week %in% c("51", "52", "53", "54", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10")),
                          "yes", "no"))
week_kuds$SpawnSeason <- as.factor(week_kuds$SpawnSeason)
boxplot(KUD95 ~ Spawn, data= week_kuds, col="deepskyblue")

boxplot(KUD50 ~ Spawn, data= week_kuds, col="green2")

#Comparar as médias dos KUDs dos individuos que se encontravam em época reprodutiva ou não

#escolhemos o teste wilcox porque não assume normalidade nos dados e é útil para grandes e pequenas amostras
wilcox.test(week_kuds$KUD95~week_kuds$Spawn)  #de acordo com o teste realizado parece não haver evidências para afirmar que a home range varia consoante a Epoca Reprodutiva, visto que o p-value toma o valor de 0.5623 que é maior do que o nivel de significância 0.05
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  week_kuds$KUD95 by week_kuds$Spawn
## W = 81731513, p-value = 0.5623
## alternative hypothesis: true location shift is not equal to 0
wilcox.test(week_kuds$KUD50~week_kuds$Spawn) #de acordo com o teste realizado parece não haver evidências para afirmar que a core area varia consoante a Epoca Reprodutiva, visto que o p-value toma o valor de 0.9972 que é maior do que o nivel de significância 0.05
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  week_kuds$KUD50 by week_kuds$Spawn
## W = 81393886, p-value = 0.9972
## alternative hypothesis: true location shift is not equal to 0
glmm_total_kud95 <- glmmTMB(KUD95 ~ Spawn + MonitArea_km2 + (1|Species) + (1|Transmitter), data=week_kuds, family = Gamma(link="log"))
summary(glmm_total_kud95)
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ Spawn + MonitArea_km2 + (1 | Species) + (1 | Transmitter)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9739.4   9788.3  -4863.7   9727.4    25606 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Species     (Intercept) 0.06316  0.2513  
##  Transmitter (Intercept) 0.08407  0.2899  
## Number of obs: 25612, groups:  Species, 30; Transmitter, 850
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0747 
## 
## Conditional model:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -0.150202   0.055654  -2.699  0.00696 ** 
## Spawnyes       0.056691   0.003803  14.908  < 2e-16 ***
## MonitArea_km2  0.029942   0.002902  10.319  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
glmm_total_kud50 <- glmmTMB(KUD50 ~ Spawn + MonitArea_km2 + (1|Species) + (1|Transmitter), data=week_kuds, family = Gamma(link="log"))
summary(glmm_total_kud50)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ Spawn + MonitArea_km2 + (1 | Species) + (1 | Transmitter)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76423.0 -76374.1  38217.5 -76435.0    25606 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Species     (Intercept) 0.04558  0.2135  
##  Transmitter (Intercept) 0.07274  0.2697  
## Number of obs: 25612, groups:  Species, 30; Transmitter, 850
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0603 
## 
## Conditional model:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -1.689189   0.048440  -34.87   <2e-16 ***
## Spawnyes       0.047096   0.003413   13.80   <2e-16 ***
## MonitArea_km2  0.023721   0.002684    8.84   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Analisys by File
# Divide dataset by 'File'
split_spawnFile <- split(week_kuds, week_kuds$File)

# Function to use the wilcoxon test in each sub-dataframe
wilcox_test_kud95 <- function(data) {
  # Verify if Spawn has exactly 2 levels (yes and no)
  if(length(unique(data$Spawn)) == 2) {
    test_result <- wilcox.test(KUD95 ~ Spawn, data = data)
    return(test_result$p.value)
  } else {
    return(NA)  # Return NA if not
  }
}

# Apply the function and get the p-values
p_values <- lapply(split_spawnFile, wilcox_test_kud95)
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
# Exhibit p-values
print(p_values)
## $Dactylopterus_volitans
## [1] NA
## 
## $Dentex_dentex1
## [1] 0.01030334
## 
## $Dentex_dentex2
## [1] 0.1078085
## 
## $Dicentrarchus_labrax1
## [1] 1.450456e-05
## 
## $Dicentrarchus_labrax2
## [1] 0.0001058427
## 
## $Diplodus_cervinus
## [1] 0.7975494
## 
## $Diplodus_sargus1
## [1] 0.1054672
## 
## $Diplodus_sargus2
## [1] 0.00252126
## 
## $Diplodus_sargus3
## [1] 0.002851269
## 
## $Diplodus_sargus4
## [1] 0.224059
## 
## $Diplodus_sargus5
## [1] 0.2519681
## 
## $Diplodus_sargus6
## [1] 0.6146928
## 
## $Diplodus_vulgaris1
## [1] 0.1348518
## 
## $Diplodus_vulgaris2
## [1] 0.3333333
## 
## $Epinephelus_marginatus1
## [1] 0.08732378
## 
## $Epinephelus_marginatus2
## [1] 0.8344767
## 
## $Epinephelus_marginatus3
## [1] 0.2898365
## 
## $Epinephelus_marginatus4
## [1] 2.725643e-05
## 
## $Gadus_morhua1
## [1] 3.51374e-06
## 
## $Gadus_morhua2
## [1] 0.3779574
## 
## $Gadus_morhua3
## [1] 3.646825e-09
## 
## $Labrus_bergylta
## [1] 1.611826e-07
## 
## $Lichia_amia
## [1] 0.03284102
## 
## $Lithognathus_mormyrus
## [1] NA
## 
## $Pagellus_erythrinus
## [1] NA
## 
## $Pagrus_pagrus1
## [1] 0.1970555
## 
## $Pagrus_pagrus2
## [1] 0.3868507
## 
## $Pomatomus_saltatrix
## [1] 0.90633
## 
## $Pseudocaranx_dentex
## [1] 0.074285
## 
## $Sciaena_umbra1
## [1] 0.100855
## 
## $Sciaena_umbra2
## [1] 0.0530303
## 
## $Scorpaena_porcus
## [1] 0.02050939
## 
## $Scorpaena_scrofa1
## [1] 0.5053349
## 
## $Scorpaena_scrofa2
## [1] 4.234985e-05
## 
## $Seriola_dumerili
## [1] 2.94866e-12
## 
## $Seriola_rivoliana
## [1] 4.398831e-06
## 
## $Serranus_atricauda
## [1] 6.274069e-08
## 
## $Serranus_cabrilla
## [1] NA
## 
## $Serranus_scriba
## [1] 0.8412007
## 
## $Solea_senegalensis
## [1] 0.03581104
## 
## $Sparisoma_cretense
## [1] 0.3443516
## 
## $Sparus_aurata1
## [1] 0.434566
## 
## $Sparus_aurata2
## [1] 0.1317029
## 
## $Sphyraena_viridensis1
## [1] 0.0005515177
## 
## $Sphyraena_viridensis2
## [1] 2.323167e-15
## 
## $Spondyliosoma_cantharus
## [1] 5.783145e-05
## 
## $Umbrina_cirrosa
## [1] NA
## 
## $Xyrichtys_novacula
## [1] NA
p_values<- t(as.data.frame(p_values))

# Function to use the wilcoxon test in each sub-dataframe
wilcox_test_kud50 <- function(data) {
  # Verify if Spawn has exactly 2 levels (yes and no)
  if(length(unique(data$Spawn)) == 2) {
    test_result <- wilcox.test(KUD50 ~ Spawn, data = data)
    return(test_result$p.value)
  } else {
    return(NA)  # Return NA if not
  }
}

# Apply the function and get the p-values
p_values <- lapply(split_spawnFile, wilcox_test_kud50)
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
# Exhibit p-values
print(p_values)
## $Dactylopterus_volitans
## [1] NA
## 
## $Dentex_dentex1
## [1] 0.0006766402
## 
## $Dentex_dentex2
## [1] 0.07389496
## 
## $Dicentrarchus_labrax1
## [1] 0.0007138528
## 
## $Dicentrarchus_labrax2
## [1] 0.002072089
## 
## $Diplodus_cervinus
## [1] 0.1787112
## 
## $Diplodus_sargus1
## [1] 0.05129045
## 
## $Diplodus_sargus2
## [1] 0.009404127
## 
## $Diplodus_sargus3
## [1] 0.005536751
## 
## $Diplodus_sargus4
## [1] 0.08955937
## 
## $Diplodus_sargus5
## [1] 0.1154605
## 
## $Diplodus_sargus6
## [1] 0.8877892
## 
## $Diplodus_vulgaris1
## [1] 0.09801128
## 
## $Diplodus_vulgaris2
## [1] 0.3333333
## 
## $Epinephelus_marginatus1
## [1] 0.0007550945
## 
## $Epinephelus_marginatus2
## [1] 0.8899593
## 
## $Epinephelus_marginatus3
## [1] 0.360626
## 
## $Epinephelus_marginatus4
## [1] 1.858381e-05
## 
## $Gadus_morhua1
## [1] 8.959164e-05
## 
## $Gadus_morhua2
## [1] 0.6036561
## 
## $Gadus_morhua3
## [1] 4.300043e-09
## 
## $Labrus_bergylta
## [1] 1.68144e-08
## 
## $Lichia_amia
## [1] 0.2620757
## 
## $Lithognathus_mormyrus
## [1] NA
## 
## $Pagellus_erythrinus
## [1] NA
## 
## $Pagrus_pagrus1
## [1] 0.04727474
## 
## $Pagrus_pagrus2
## [1] 0.2663605
## 
## $Pomatomus_saltatrix
## [1] 0.9564324
## 
## $Pseudocaranx_dentex
## [1] 0.06130092
## 
## $Sciaena_umbra1
## [1] 0.1403615
## 
## $Sciaena_umbra2
## [1] 0.259324
## 
## $Scorpaena_porcus
## [1] 0.01589705
## 
## $Scorpaena_scrofa1
## [1] 0.8384118
## 
## $Scorpaena_scrofa2
## [1] 0.0001346187
## 
## $Seriola_dumerili
## [1] 7.89286e-12
## 
## $Seriola_rivoliana
## [1] 0.1443612
## 
## $Serranus_atricauda
## [1] 1.429118e-09
## 
## $Serranus_cabrilla
## [1] NA
## 
## $Serranus_scriba
## [1] 0.8428269
## 
## $Solea_senegalensis
## [1] 0.1996681
## 
## $Sparisoma_cretense
## [1] 0.6576976
## 
## $Sparus_aurata1
## [1] 0.6422533
## 
## $Sparus_aurata2
## [1] 0.005342033
## 
## $Sphyraena_viridensis1
## [1] 0.001669869
## 
## $Sphyraena_viridensis2
## [1] 6.294566e-07
## 
## $Spondyliosoma_cantharus
## [1] 0.0001120269
## 
## $Umbrina_cirrosa
## [1] NA
## 
## $Xyrichtys_novacula
## [1] NA
p_values<- t(as.data.frame(p_values))
#Glmm KUD95 for each File

data_dentex_dentex1 <- subset(week_kuds, File == "Dentex_dentex1")

glmm_dentex_dentex1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dentex_dentex1, family = Gamma(link="log"))
summary(glmm_dentex_dentex1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex1
## 
##      AIC      BIC   logLik deviance df.resid 
##      3.3     21.9      2.4     -4.7      774 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.03892  0.1973  
## Number of obs: 778, groups:  Transmitter, 19
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0384 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.11417    0.04733   2.412   0.0159 *  
## Spawnyes     0.06093    0.01442   4.227 2.37e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex1$KUD95 ~ data_dentex_dentex1$Spawn)

#################################################################################
data_dentex_dentex2 <- subset(week_kuds, File == "Dentex_dentex2")

glmm_dentex_dentex2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dentex_dentex2, family = Gamma(link="log"))
summary(glmm_dentex_dentex2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex2
## 
##      AIC      BIC   logLik deviance df.resid 
##   1008.2   1025.8   -500.1   1000.2      595 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1173   0.3426  
## Number of obs: 599, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.138 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.28149    0.09143   3.079  0.00208 ** 
## Spawnyes     0.15458    0.03177   4.865 1.14e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex2$KUD95 ~ data_dentex_dentex2$Spawn)

#################################################################################
data_dicentrarchus_labrax1 <- subset(week_kuds, File == "Dicentrarchus_labrax1")

glmm_dicentrarchus_labrax1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax1, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax1
## 
##      AIC      BIC   logLik deviance df.resid 
##    753.4    772.4   -372.7    745.4      850 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1006   0.3171  
## Number of obs: 854, groups:  Transmitter, 93
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0931 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.16255    0.03588   4.531 5.88e-06 ***
## Spawnyes    -0.04691    0.06031  -0.778    0.437    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax1$KUD95 ~ data_dicentrarchus_labrax1$Spawn)

#################################################################################
data_dicentrarchus_labrax2 <- subset(week_kuds, File == "Dicentrarchus_labrax2")

glmm_dicentrarchus_labrax2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax2, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax2
## 
##      AIC      BIC   logLik deviance df.resid 
##   1620.9   1638.7   -806.4   1612.9      633 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.2229   0.4722  
## Number of obs: 637, groups:  Transmitter, 28
## 
## Dispersion estimate for Gamma family (sigma^2): 0.188 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.62269    0.09675   6.436 1.22e-10 ***
## Spawnyes     0.26338    0.03995   6.593 4.31e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax2$KUD95 ~ data_dicentrarchus_labrax2$Spawn)

#################################################################################
data_diplodus_cervinus <- subset(week_kuds, File == "Diplodus_cervinus")

glmm_diplodus_cervinus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_cervinus, family = Gamma(link="log"))
summary(glmm_diplodus_cervinus)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_cervinus
## 
##      AIC      BIC   logLik deviance df.resid 
##    164.8    175.0    -78.4    156.8       90 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.147    0.3834  
## Number of obs: 94, groups:  Transmitter, 4
## 
## Dispersion estimate for Gamma family (sigma^2): 0.15 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.31443    0.20324   1.547  0.12185   
## Spawnyes    -0.25631    0.09128  -2.808  0.00498 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_cervinus$KUD95 ~ data_diplodus_cervinus$Spawn)

#################################################################################
data_diplodus_sargus1 <- subset(week_kuds, File == "Diplodus_sargus1")

glmm_diplodus_sargus1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus1, family = Gamma(link="log"))
summary(glmm_diplodus_sargus1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus1
## 
##      AIC      BIC   logLik deviance df.resid 
##    143.7    159.1    -67.8    135.7      347 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07295  0.2701  
## Number of obs: 351, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0723 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.042330   0.074203   0.571    0.568
## Spawnyes    -0.009141   0.033723  -0.271    0.786
boxplot(data_diplodus_sargus1$KUD95 ~ data_diplodus_sargus1$Spawn)

#################################################################################
data_diplodus_sargus2 <- subset(week_kuds, File == "Diplodus_sargus2")

glmm_diplodus_sargus2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus2, family = Gamma(link="log"))
summary(glmm_diplodus_sargus2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus2
## 
##      AIC      BIC   logLik deviance df.resid 
##   -653.2   -635.2    330.6   -661.2      656 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.01889  0.1374  
## Number of obs: 660, groups:  Transmitter, 20
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0237 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.02555    0.03412   0.749   0.4539  
## Spawnyes    -0.02520    0.01480  -1.703   0.0886 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus2$KUD95 ~ data_diplodus_sargus2$Spawn)

#################################################################################
data_diplodus_sargus3 <- subset(week_kuds, File == "Diplodus_sargus3")

glmm_diplodus_sargus3 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus3, family = Gamma(link="log"))
summary(glmm_diplodus_sargus3)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus3
## 
##      AIC      BIC   logLik deviance df.resid 
##   -177.8   -168.2     92.9   -185.8       76 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 0.0006858 0.02619 
## Number of obs: 80, groups:  Transmitter, 4
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00797 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.22252    0.01878 -11.851  < 2e-16 ***
## Spawnyes     0.08364    0.02016   4.148 3.36e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus3$KUD95 ~ data_diplodus_sargus3$Spawn)

#################################################################################
data_diplodus_sargus4 <- subset(week_kuds, File == "Diplodus_sargus4")

glmm_diplodus_sargus4 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus4, family = Gamma(link="log"))
summary(glmm_diplodus_sargus4)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus4
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1780.1  -1758.9    894.1  -1788.1     1470 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02426  0.1558  
## Number of obs: 1474, groups:  Transmitter, 41
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0172 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.055328   0.025343  -2.183    0.029 *
## Spawnyes    -0.004623   0.006936  -0.666    0.505  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus4$KUD95 ~ data_diplodus_sargus4$Spawn)

#################################################################################
data_diplodus_sargus5 <- subset(week_kuds, File == "Diplodus_sargus5")

glmm_diplodus_sargus5 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus5, family = Gamma(link="log"))
summary(glmm_diplodus_sargus5)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus5
## 
##      AIC      BIC   logLik deviance df.resid 
##    291.3    311.3   -141.6    283.3     1098 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02454  0.1566  
## Number of obs: 1102, groups:  Transmitter, 73
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0703 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.03582    0.02336   1.533    0.125
## Spawnyes    -0.01763    0.01790  -0.985    0.325
boxplot(data_diplodus_sargus5$KUD95 ~ data_diplodus_sargus5$Spawn)

#################################################################################
data_diplodus_sargus6 <- subset(week_kuds, File == "Diplodus_sargus6")

glmm_diplodus_sargus6 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus6, family = Gamma(link="log"))
summary(glmm_diplodus_sargus6)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus6
## 
##      AIC      BIC   logLik deviance df.resid 
##    -37.4    -30.5     22.7    -45.4       37 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1828   0.4275  
## Number of obs: 41, groups:  Transmitter, 6
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0165 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.06001    0.17875   0.336    0.737
## Spawnyes    -0.02549    0.05101  -0.500    0.617
boxplot(data_diplodus_sargus6$KUD95 ~ data_diplodus_sargus6$Spawn)

#################################################################################
data_diplodus_vulgaris1 <- subset(week_kuds, File == "Diplodus_vulgaris1")

glmm_diplodus_vulgaris1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris1, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris1
## 
##      AIC      BIC   logLik deviance df.resid 
##     -7.4      0.0      7.7    -15.4       42 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.03967  0.1992  
## Number of obs: 46, groups:  Transmitter, 9
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0369 
## 
## Conditional model:
##               Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.0009159  0.0769672   0.012   0.9905  
## Spawnyes    -0.1871862  0.1024773  -1.827   0.0678 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris1$KUD95 ~ data_diplodus_vulgaris1$Spawn)

#################################################################################
data_diplodus_vulgaris2 <- subset(week_kuds, File == "Diplodus_vulgaris2")

glmm_diplodus_vulgaris2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris2, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris2
## 
##      AIC      BIC   logLik deviance df.resid 
##     -7.7    -10.1      7.8    -15.7        0 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.006177 0.07859 
## Number of obs: 4, groups:  Transmitter, 2
## 
## Dispersion estimate for Gamma family (sigma^2): 7.18e-05 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.53980    0.05590    9.66   <2e-16 ***
## Spawnyes    -0.72481    0.01044  -69.42   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris2$KUD95 ~ data_diplodus_vulgaris2$Spawn)

#################################################################################
data_epinephelus_marginatus1 <- subset(week_kuds, File == "Epinephelus_marginatus1")

glmm_epinephelus_marginatus1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus1, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus1
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3878.8  -3856.3   1943.4  -3886.8     2051 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.004326 0.06577 
## Number of obs: 2055, groups:  Transmitter, 11
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0131 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.232142   0.020292 -11.440  < 2e-16 ***
## Spawnyes     0.035552   0.005102   6.969  3.2e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus1$KUD95 ~ data_epinephelus_marginatus1$Spawn)

#################################################################################
data_epinephelus_marginatus2 <- subset(week_kuds, File == "Epinephelus_marginatus2")

glmm_epinephelus_marginatus2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus2, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus2
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1926.5  -1910.2    967.3  -1934.5      433 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 5.013e-05 0.00708 
## Number of obs: 437, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00114 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.260076   0.002685  -96.87  < 2e-16 ***
## Spawnyes     0.010431   0.003580    2.91  0.00358 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus2$KUD95 ~ data_epinephelus_marginatus2$Spawn)

#################################################################################
data_epinephelus_marginatus3 <- subset(week_kuds, File == "Epinephelus_marginatus3")

glmm_epinephelus_marginatus3 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus3, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus3)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus3
## 
##      AIC      BIC   logLik deviance df.resid 
##   -689.6   -675.9    348.8   -697.6      223 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 0.0002354 0.01534 
## Number of obs: 227, groups:  Transmitter, 4
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00425 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.249606   0.010156 -24.576  < 2e-16 ***
## Spawnyes     0.023242   0.008761   2.653  0.00798 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus3$KUD95 ~ data_epinephelus_marginatus3$Spawn)

#################################################################################
data_epinephelus_marginatus4 <- subset(week_kuds, File == "Epinephelus_marginatus4")

glmm_epinephelus_marginatus4 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus4, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus4)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus4
## 
##      AIC      BIC   logLik deviance df.resid 
##    120.0    134.7    -56.0    112.0      289 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1211   0.348   
## Number of obs: 293, groups:  Transmitter, 17
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0814 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.10990    0.08870  -1.239    0.215    
## Spawnyes     0.17134    0.03597   4.763  1.9e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus4$KUD95 ~ data_epinephelus_marginatus4$Spawn)

#################################################################################
data_gadus_morhua1 <- subset(week_kuds, File == "Gadus_morhua1")

glmm_gadus_morhua1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_gadus_morhua1, family = Gamma(link="log"))
summary(glmm_gadus_morhua1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua1
## 
##      AIC      BIC   logLik deviance df.resid 
##    307.2    328.8   -149.6    299.2     1631 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.03343  0.1828  
## Number of obs: 1635, groups:  Transmitter, 60
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0566 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.01418    0.02752   0.515    0.606    
## Spawnyes     0.07521    0.01442   5.217 1.82e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua1$KUD95 ~ data_gadus_morhua1$Spawn)

#################################################################################
data_gadus_morhua2 <- subset(week_kuds, File == "Gadus_morhua2")

glmm_gadus_morhua2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_gadus_morhua2, family = Gamma(link="log"))
summary(glmm_gadus_morhua2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua2
## 
##      AIC      BIC   logLik deviance df.resid 
##    197.1    217.2    -94.5    189.1     1132 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.04499  0.2121  
## Number of obs: 1136, groups:  Transmitter, 56
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0684 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.004647   0.037490  -0.124    0.901
## Spawnyes    -0.014876   0.024961  -0.596    0.551
boxplot(data_gadus_morhua2$KUD95 ~ data_gadus_morhua2$Spawn)

#################################################################################
data_gadus_morhua3 <- subset(week_kuds, File == "Gadus_morhua3")

glmm_gadus_morhua3 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_gadus_morhua3, family = Gamma(link="log"))
summary(glmm_gadus_morhua3)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua3
## 
##      AIC      BIC   logLik deviance df.resid 
##   -741.7   -725.8    374.9   -749.7      395 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.004891 0.06994 
## Number of obs: 399, groups:  Transmitter, 29
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0111 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.17004    0.01833  -9.278  < 2e-16 ***
## Spawnyes     0.04018    0.01253   3.206  0.00135 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua3$KUD95 ~ data_gadus_morhua3$Spawn)

#################################################################################
data_labrus_bergylta <- subset(week_kuds, File == "Labrus_bergylta")

glmm_labrus_bergylta <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_labrus_bergylta, family = Gamma(link="log"))
summary(glmm_labrus_bergylta)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_labrus_bergylta
## 
##      AIC      BIC   logLik deviance df.resid 
##  -2897.4  -2878.7   1452.7  -2905.4      789 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.001729 0.04158 
## Number of obs: 793, groups:  Transmitter, 25
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00195 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.193686   0.008599 -22.523  < 2e-16 ***
## Spawnyes     0.022007   0.003162   6.959 3.42e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_labrus_bergylta$KUD95 ~ data_labrus_bergylta$Spawn)

#################################################################################
data_lichia_amia<- subset(week_kuds, File == "Lichia_amia")

glmm_lichia_amia <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_lichia_amia, family = Gamma(link="log"))
summary(glmm_lichia_amia)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_lichia_amia
## 
##      AIC      BIC   logLik deviance df.resid 
##     87.5     92.8    -39.7     79.5       24 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev. 
##  Transmitter (Intercept) 1.717e-11 4.144e-06
## Number of obs: 28, groups:  Transmitter, 1
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0735 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.9167     0.1356   6.762 1.36e-11 ***
## Spawnyes      0.4826     0.1464   3.296  0.00098 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_lichia_amia$KUD95 ~ data_lichia_amia$Spawn)

#################################################################################
data_pagrus_pagrus1 <- subset(week_kuds, File == "Pagrus_pagrus1")

glmm_pagrus_pagrus1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus1, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -192.5   -174.8    100.3   -200.5      614 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.05044  0.2246  
## Number of obs: 618, groups:  Transmitter, 20
## 
## Dispersion estimate for Gamma family (sigma^2): 0.048 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -0.08623    0.05379  -1.603  0.10888   
## Spawnyes     0.04813    0.01858   2.591  0.00957 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pagrus_pagrus1$KUD95 ~ data_pagrus_pagrus1$Spawn)

#################################################################################
data_pagrus_pagrus2 <- subset(week_kuds, File == "Pagrus_pagrus2")

glmm_pagrus_pagrus2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus2, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus2
## 
##      AIC      BIC   logLik deviance df.resid 
##     17.3     23.6     -4.6      9.3       32 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.7809   0.8837  
## Number of obs: 36, groups:  Transmitter, 5
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0428 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.46922    0.40388   1.162    0.245
## Spawnyes     0.02786    0.07300   0.382    0.703
boxplot(data_pagrus_pagrus2$KUD95 ~ data_pagrus_pagrus2$Spawn)

#################################################################################
data_pomatomus_saltatrix <- subset(week_kuds, File == "Pomatomus_saltatrix")

glmm_pomatomus_saltatrix <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pomatomus_saltatrix, family = Gamma(link="log"))
summary(glmm_pomatomus_saltatrix)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pomatomus_saltatrix
## 
##      AIC      BIC   logLik deviance df.resid 
##    622.8    635.1   -307.4    614.8      155 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.05284  0.2299  
## Number of obs: 159, groups:  Transmitter, 8
## 
## Dispersion estimate for Gamma family (sigma^2): 0.404 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  1.10622    0.10263  10.778   <2e-16 ***
## Spawnyes    -0.05879    0.13605  -0.432    0.666    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pomatomus_saltatrix$KUD95 ~ data_pomatomus_saltatrix$Spawn)

#################################################################################
data_pseudocaranx_dentex <- subset(week_kuds, File == "Pseudocaranx_dentex")

glmm_pseudocaranx_dentex <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pseudocaranx_dentex, family = Gamma(link="log"))
summary(glmm_pseudocaranx_dentex)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pseudocaranx_dentex
## 
##      AIC      BIC   logLik deviance df.resid 
##   1718.0   1739.3   -855.0   1710.0     1523 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1472   0.3837  
## Number of obs: 1527, groups:  Transmitter, 31
## 
## Dispersion estimate for Gamma family (sigma^2): 0.143 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.10946    0.07357   1.488  0.13681   
## Spawnyes     0.06570    0.02074   3.168  0.00154 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pseudocaranx_dentex$KUD95 ~ data_pseudocaranx_dentex$Spawn)

#################################################################################
data_sciaena_umbra1 <- subset(week_kuds, File == "Sciaena_umbra1")

glmm_sciaena_umbra1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra1, family = Gamma(link="log"))
summary(glmm_sciaena_umbra1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -523.4   -512.0    265.7   -531.4      125 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.008098 0.08999 
## Number of obs: 129, groups:  Transmitter, 15
## 
## Dispersion estimate for Gamma family (sigma^2): 0.000979 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.178216   0.024450  -7.289 3.12e-13 ***
## Spawnyes    -0.009497   0.008092  -1.174    0.241    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra1$KUD95 ~ data_sciaena_umbra1$Spawn)

#################################################################################
data_sciaena_umbra2 <- subset(week_kuds, File == "Sciaena_umbra2")

glmm_sciaena_umbra2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra2, family = Gamma(link="log"))
summary(glmm_sciaena_umbra2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra2
## 
##      AIC      BIC   logLik deviance df.resid 
##     16.2     18.8     -4.1      8.2       10 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 4.245e-12 2.06e-06
## Number of obs: 14, groups:  Transmitter, 1
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0555 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.48445    0.08901   5.443 5.25e-08 ***
## Spawnyes    -0.29053    0.12588  -2.308    0.021 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra2$KUD95 ~ data_sciaena_umbra2$Spawn)

#################################################################################
data_scorpaena_porcus <- subset(week_kuds, File == "Scorpaena_porcus")

glmm_scorpaena_porcus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_scorpaena_porcus, family = Gamma(link="log"))
summary(glmm_scorpaena_porcus)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_porcus
## 
##      AIC      BIC   logLik deviance df.resid 
##   -143.2   -135.4     75.6   -151.2       48 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 0.0005034 0.02244 
## Number of obs: 52, groups:  Transmitter, 13
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00441 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.17716    0.01605 -11.038   <2e-16 ***
## Spawnyes    -0.04611    0.02097  -2.199   0.0279 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_porcus$KUD95 ~ data_scorpaena_porcus$Spawn)

#################################################################################
data_scorpaena_scrofa1 <- subset(week_kuds, File == "Scorpaena_scrofa1")

glmm_scorpaena_scrofa1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa1, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -202.8   -194.5    105.4   -210.8       54 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.002972 0.05451 
## Number of obs: 58, groups:  Transmitter, 7
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0017 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.18855    0.02615  -7.210 5.61e-13 ***
## Spawnyes    -0.01773    0.01702  -1.042    0.297    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa1$KUD95 ~ data_scorpaena_scrofa1$Spawn)

#################################################################################
data_scorpaena_scrofa2 <- subset(week_kuds, File == "Scorpaena_scrofa2")

glmm_scorpaena_scrofa2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa2, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa2
## 
##      AIC      BIC   logLik deviance df.resid 
##   -361.8   -344.9    184.9   -369.8      504 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.0222   0.149   
## Number of obs: 508, groups:  Transmitter, 11
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0308 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.22672    0.04739  -4.784 1.72e-06 ***
## Spawnyes     0.16755    0.01625  10.313  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa2$KUD95 ~ data_scorpaena_scrofa2$Spawn)

#################################################################################
data_seriola_dumerili <- subset(week_kuds, File == "Seriola_dumerili")

glmm_seriola_dumerili <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_seriola_dumerili, family = Gamma(link="log"))
summary(glmm_seriola_dumerili)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_dumerili
## 
##      AIC      BIC   logLik deviance df.resid 
##   1039.4   1054.9   -515.7   1031.4      352 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.06693  0.2587  
## Number of obs: 356, groups:  Transmitter, 8
## 
## Dispersion estimate for Gamma family (sigma^2):  0.2 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.67695    0.10122   6.688 2.26e-11 ***
## Spawnyes     0.42055    0.04952   8.493  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_dumerili$KUD95 ~ data_seriola_dumerili$Spawn)

#################################################################################
data_seriola_rivoliana <- subset(week_kuds, File == "Seriola_rivoliana")

glmm_seriola_rivoliana <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_seriola_rivoliana, family = Gamma(link="log"))
summary(glmm_seriola_rivoliana)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_rivoliana
## 
##      AIC      BIC   logLik deviance df.resid 
##   1065.4   1089.2   -528.7   1057.4     2783 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.01212  0.1101  
## Number of obs: 2787, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0968 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.05972    0.02911  -2.052   0.0402 *  
## Spawnyes     0.04774    0.01209   3.949 7.86e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_rivoliana$KUD95 ~ data_seriola_rivoliana$Spawn)

#################################################################################
data_serranus_atricauda <- subset(week_kuds, File == "Serranus_atricauda")

glmm_serranus_atricauda <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_serranus_atricauda, family = Gamma(link="log"))
summary(glmm_serranus_atricauda)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_atricauda
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3445.6  -3427.7   1726.8  -3453.6      646 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 8.305e-05 0.009113
## Number of obs: 650, groups:  Transmitter, 9
## 
## Dispersion estimate for Gamma family (sigma^2): 0.000474 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.263748   0.003475  -75.89   <2e-16 ***
## Spawnyes    -0.002961   0.001762   -1.68   0.0929 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_atricauda$KUD95 ~ data_serranus_atricauda$Spawn)

#################################################################################
data_serranus_scriba <- subset(week_kuds, File == "Serranus_scriba")

glmm_serranus_scriba <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_serranus_scriba, family = Gamma(link="log"))
summary(glmm_serranus_scriba)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_scriba
## 
##      AIC      BIC   logLik deviance df.resid 
##    -30.5    -25.3     19.3    -38.5       23 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02947  0.1717  
## Number of obs: 27, groups:  Transmitter, 10
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00803 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.116927   0.082518  -1.417    0.156
## Spawnyes    -0.002848   0.063364  -0.045    0.964
boxplot(data_serranus_scriba$KUD95 ~ data_serranus_scriba$Spawn)

#################################################################################
data_solea_senegalensis <- subset(week_kuds, File == "Solea_senegalensis")

glmm_solea_senegalensis <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_solea_senegalensis, family = Gamma(link="log"))
summary(glmm_solea_senegalensis)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_solea_senegalensis
## 
##      AIC      BIC   logLik deviance df.resid 
##    -38.6    -24.7     23.3    -46.6      233 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.05953  0.244   
## Number of obs: 237, groups:  Transmitter, 22
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0456 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.08365    0.05788   1.445    0.148
## Spawnyes    -0.03383    0.03830  -0.883    0.377
boxplot(data_solea_senegalensis$KUD95 ~ data_solea_senegalensis$Spawn)

#################################################################################
data_sparisoma_cretense <- subset(week_kuds, File == "Sparisoma_cretense")

glmm_sparisoma_cretense <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sparisoma_cretense, family = Gamma(link="log"))
summary(glmm_sparisoma_cretense)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sparisoma_cretense
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1031.2  -1012.6    519.6  -1039.2      765 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.01394  0.1181  
## Number of obs: 769, groups:  Transmitter, 10
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0195 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -0.117408   0.038447  -3.054  0.00226 **
## Spawnyes    -0.002199   0.010253  -0.214  0.83017   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparisoma_cretense$KUD95 ~ data_sparisoma_cretense$Spawn)

#################################################################################
data_sparus_aurata1 <- subset(week_kuds, File == "Sparus_aurata1")

glmm_sparus_aurata1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sparus_aurata1, family = Gamma(link="log"))
summary(glmm_sparus_aurata1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -517.9   -506.5    262.9   -525.9      123 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.001269 0.03563 
## Number of obs: 127, groups:  Transmitter, 7
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00129 
## 
## Conditional model:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.2336301  0.0149273 -15.651   <2e-16 ***
## Spawnyes     0.0008848  0.0076277   0.116    0.908    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata1$KUD95 ~ data_sparus_aurata1$Spawn)

#################################################################################
data_sparus_aurata2 <- subset(week_kuds, File == "Sparus_aurata2")

glmm_sparus_aurata2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sparus_aurata2, family = Gamma(link="log"))
summary(glmm_sparus_aurata2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata2
## 
##      AIC      BIC   logLik deviance df.resid 
##   1502.0   1522.2   -747.0   1494.0     1129 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.3298   0.5742  
## Number of obs: 1133, groups:  Transmitter, 43
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0997 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.60117    0.09069   6.629 3.37e-11 ***
## Spawnyes     0.02415    0.02409   1.002    0.316    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata2$KUD95 ~ data_sparus_aurata2$Spawn)

#################################################################################
data_sphyraena_viridensis1 <- subset(week_kuds, File == "Sphyraena_viridensis1")

glmm_sphyraena_viridensis1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis1, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis1
## 
##      AIC      BIC   logLik deviance df.resid 
##    159.8    180.5    -75.9    151.8     1294 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02137  0.1462  
## Number of obs: 1298, groups:  Transmitter, 13
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0778 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.053769   0.043035  -1.249    0.212
## Spawnyes     0.009959   0.015824   0.629    0.529
boxplot(data_sphyraena_viridensis1$KUD95 ~ data_sphyraena_viridensis1$Spawn)

#################################################################################
data_sphyraena_viridensis2 <- subset(week_kuds, File == "Sphyraena_viridensis2")

glmm_sphyraena_viridensis2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis2, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis2
## 
##      AIC      BIC   logLik deviance df.resid 
##   1765.4   1782.7   -878.7   1757.4      554 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1546   0.3932  
## Number of obs: 558, groups:  Transmitter, 17
## 
## Dispersion estimate for Gamma family (sigma^2): 0.277 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.51837    0.10258   5.053 4.34e-07 ***
## Spawnyes     0.56290    0.04677  12.037  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sphyraena_viridensis2$KUD95 ~ data_sphyraena_viridensis2$Spawn)

#################################################################################
data_spondyliosoma_cantharus <- subset(week_kuds, File == "Spondyliosoma_cantharus")

glmm_spondyliosoma_cantharus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_spondyliosoma_cantharus, family = Gamma(link="log"))
summary(glmm_spondyliosoma_cantharus)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_spondyliosoma_cantharus
## 
##      AIC      BIC   logLik deviance df.resid 
##    142.7    160.7    -67.4    134.7      659 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.05021  0.2241  
## Number of obs: 663, groups:  Transmitter, 21
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0657 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.07551    0.05403   1.398  0.16219   
## Spawnyes    -0.05671    0.02129  -2.664  0.00773 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_spondyliosoma_cantharus$KUD95 ~ data_spondyliosoma_cantharus$Spawn)

#################################################################################
#Glmm KUD95 for each Species

data_dentex_dentex <- subset(week_kuds, Species == "Dden")

glmm_dentex_dentex <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dentex_dentex, family = Gamma(link="log"))
summary(glmm_dentex_dentex)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex
## 
##      AIC      BIC   logLik deviance df.resid 
##   1297.4   1318.3   -644.7   1289.4     1373 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08367  0.2893  
## Number of obs: 1377, groups:  Transmitter, 35
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0829 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.19110    0.05137   3.720 0.000199 ***
## Spawnyes     0.10072    0.01603   6.283 3.33e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex$KUD95 ~ data_dentex_dentex$Spawn)

#################################################################################
data_dicentrarchus_labrax <- subset(week_kuds, Species == "Dlab")

glmm_dicentrarchus_labrax <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax
## 
##      AIC      BIC   logLik deviance df.resid 
##   2505.5   2526.7  -1248.8   2497.5     1487 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1702   0.4125  
## Number of obs: 1491, groups:  Transmitter, 121
## 
## Dispersion estimate for Gamma family (sigma^2): 0.137 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.27403    0.04054    6.76 1.38e-11 ***
## Spawnyes     0.23486    0.03086    7.61 2.75e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax$KUD95 ~ data_dicentrarchus_labrax$Spawn)

#################################################################################
data_diplodus_cervinus <- subset(week_kuds, Species == "Dcer")

glmm_diplodus_cervinus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_cervinus, family = Gamma(link="log"))
summary(glmm_diplodus_cervinus)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_cervinus
## 
##      AIC      BIC   logLik deviance df.resid 
##    164.8    175.0    -78.4    156.8       90 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.147    0.3834  
## Number of obs: 94, groups:  Transmitter, 4
## 
## Dispersion estimate for Gamma family (sigma^2): 0.15 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.31443    0.20324   1.547  0.12185   
## Spawnyes    -0.25631    0.09128  -2.808  0.00498 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_cervinus$KUD95 ~ data_diplodus_cervinus$Spawn)

#################################################################################
data_diplodus_sargus <- subset(week_kuds, Species == "Dsar")

glmm_diplodus_sargus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus, family = Gamma(link="log"))
summary(glmm_diplodus_sargus)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1391.2  -1366.3    699.6  -1399.2     3704 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.03439  0.1854  
## Number of obs: 3708, groups:  Transmitter, 160
## 
## Dispersion estimate for Gamma family (sigma^2): 0.039 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.002815   0.016056   0.175    0.861
## Spawnyes    -0.008657   0.007103  -1.219    0.223
boxplot(data_diplodus_sargus$KUD95 ~ data_diplodus_sargus$Spawn)

#################################################################################
data_diplodus_vulgaris <- subset(week_kuds, Species == "Dvul")

glmm_diplodus_vulgaris <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris
## 
##      AIC      BIC   logLik deviance df.resid 
##     -0.5      7.1      4.3     -8.5       46 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.05271  0.2296  
## Number of obs: 50, groups:  Transmitter, 11
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0396 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.06833    0.07991   0.855  0.39249   
## Spawnyes    -0.26811    0.09239  -2.902  0.00371 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris$KUD95 ~ data_diplodus_vulgaris$Spawn)

#################################################################################
data_epinephelus_marginatus <- subset(week_kuds, Species == "Emar")

glmm_epinephelus_marginatus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus
## 
##      AIC      BIC   logLik deviance df.resid 
##  -4624.1  -4600.1   2316.1  -4632.1     3008 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.06052  0.246   
## Number of obs: 3012, groups:  Transmitter, 48
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0175 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.18081    0.03585  -5.043 4.58e-07 ***
## Spawnyes     0.04345    0.00498   8.725  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus$KUD95 ~ data_epinephelus_marginatus$Spawn)

#################################################################################
data_gadus_morhua <- subset(week_kuds, Species == "Gmor")

glmm_gadus_morhua <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_gadus_morhua, family = Gamma(link="log"))
summary(glmm_gadus_morhua)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua
## 
##      AIC      BIC   logLik deviance df.resid 
##    136.8    161.0    -64.4    128.8     3166 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.03853  0.1963  
## Number of obs: 3170, groups:  Transmitter, 145
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0555 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.03835    0.01973  -1.943    0.052 .  
## Spawnyes     0.04808    0.01104   4.357 1.32e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua$KUD95 ~ data_gadus_morhua$Spawn)

#################################################################################
data_labrus_bergylta <- subset(week_kuds, Species == "Lber")

glmm_labrus_bergylta <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_labrus_bergylta, family = Gamma(link="log"))
summary(glmm_labrus_bergylta)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_labrus_bergylta
## 
##      AIC      BIC   logLik deviance df.resid 
##  -2897.4  -2878.7   1452.7  -2905.4      789 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.001729 0.04158 
## Number of obs: 793, groups:  Transmitter, 25
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00195 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.193686   0.008599 -22.523  < 2e-16 ***
## Spawnyes     0.022007   0.003162   6.959 3.42e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_labrus_bergylta$KUD95 ~ data_labrus_bergylta$Spawn)

#################################################################################
data_lichia_amia<- subset(week_kuds, Species == "Lami")

glmm_lichia_amia <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_lichia_amia, family = Gamma(link="log"))
summary(glmm_lichia_amia)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_lichia_amia
## 
##      AIC      BIC   logLik deviance df.resid 
##     87.5     92.8    -39.7     79.5       24 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev. 
##  Transmitter (Intercept) 1.717e-11 4.144e-06
## Number of obs: 28, groups:  Transmitter, 1
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0735 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.9167     0.1356   6.762 1.36e-11 ***
## Spawnyes      0.4826     0.1464   3.296  0.00098 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_lichia_amia$KUD95 ~ data_lichia_amia$Spawn)

#################################################################################
data_pagrus_pagrus <- subset(week_kuds, Species == "Ppag")

glmm_pagrus_pagrus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus
## 
##      AIC      BIC   logLik deviance df.resid 
##   -159.5   -141.6     83.8   -167.5      650 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.2255   0.4748  
## Number of obs: 654, groups:  Transmitter, 25
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0479 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.01910    0.09772   0.195  0.84505   
## Spawnyes     0.04928    0.01805   2.730  0.00634 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pagrus_pagrus$KUD95 ~ data_pagrus_pagrus$Spawn)

#################################################################################
data_pomatomus_saltatrix <- subset(week_kuds, Species == "Psal")

glmm_pomatomus_saltatrix <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pomatomus_saltatrix, family = Gamma(link="log"))
summary(glmm_pomatomus_saltatrix)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pomatomus_saltatrix
## 
##      AIC      BIC   logLik deviance df.resid 
##    622.8    635.1   -307.4    614.8      155 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.05284  0.2299  
## Number of obs: 159, groups:  Transmitter, 8
## 
## Dispersion estimate for Gamma family (sigma^2): 0.404 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  1.10622    0.10263  10.778   <2e-16 ***
## Spawnyes    -0.05879    0.13605  -0.432    0.666    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pomatomus_saltatrix$KUD95 ~ data_pomatomus_saltatrix$Spawn)

#################################################################################
data_pseudocaranx_dentex <- subset(week_kuds, Species == "Pden")

glmm_pseudocaranx_dentex <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pseudocaranx_dentex, family = Gamma(link="log"))
summary(glmm_pseudocaranx_dentex)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pseudocaranx_dentex
## 
##      AIC      BIC   logLik deviance df.resid 
##   1718.0   1739.3   -855.0   1710.0     1523 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1472   0.3837  
## Number of obs: 1527, groups:  Transmitter, 31
## 
## Dispersion estimate for Gamma family (sigma^2): 0.143 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.10946    0.07357   1.488  0.13681   
## Spawnyes     0.06570    0.02074   3.168  0.00154 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pseudocaranx_dentex$KUD95 ~ data_pseudocaranx_dentex$Spawn)

#################################################################################
data_sciaena_umbra <- subset(week_kuds, Species == "Sumb")

glmm_sciaena_umbra <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra, family = Gamma(link="log"))
summary(glmm_sciaena_umbra)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra
## 
##      AIC      BIC   logLik deviance df.resid 
##   -270.3   -258.5    139.2   -278.3      139 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02421  0.1556  
## Number of obs: 143, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00873 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -0.10460    0.04372  -2.393  0.01672 * 
## Spawnyes    -0.06285    0.02144  -2.931  0.00338 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra$KUD95 ~ data_sciaena_umbra$Spawn)

#################################################################################
data_scorpaena_porcus <- subset(week_kuds, Species == "Spor")

glmm_scorpaena_porcus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_scorpaena_porcus, family = Gamma(link="log"))
summary(glmm_scorpaena_porcus)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_porcus
## 
##      AIC      BIC   logLik deviance df.resid 
##   -143.2   -135.4     75.6   -151.2       48 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 0.0005034 0.02244 
## Number of obs: 52, groups:  Transmitter, 13
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00441 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.17716    0.01605 -11.038   <2e-16 ***
## Spawnyes    -0.04611    0.02097  -2.199   0.0279 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_porcus$KUD95 ~ data_scorpaena_porcus$Spawn)

#################################################################################
data_scorpaena_scrofa <- subset(week_kuds, Species == "Sscr")

glmm_scorpaena_scrofa <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -202.8   -194.5    105.4   -210.8       54 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.002972 0.05451 
## Number of obs: 58, groups:  Transmitter, 7
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0017 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.18855    0.02615  -7.210 5.61e-13 ***
## Spawnyes    -0.01773    0.01702  -1.042    0.297    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa$KUD95 ~ data_scorpaena_scrofa$Spawn)

#################################################################################
data_seriola_dumerili <- subset(week_kuds, Species == "Sdum")

glmm_seriola_dumerili <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_seriola_dumerili, family = Gamma(link="log"))
summary(glmm_seriola_dumerili)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_dumerili
## 
##      AIC      BIC   logLik deviance df.resid 
##   1039.4   1054.9   -515.7   1031.4      352 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.06693  0.2587  
## Number of obs: 356, groups:  Transmitter, 8
## 
## Dispersion estimate for Gamma family (sigma^2):  0.2 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.67695    0.10122   6.688 2.26e-11 ***
## Spawnyes     0.42055    0.04952   8.493  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_dumerili$KUD95 ~ data_seriola_dumerili$Spawn)

#################################################################################
data_seriola_rivoliana <- subset(week_kuds, Species == "Sriv")

glmm_seriola_rivoliana <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_seriola_rivoliana, family = Gamma(link="log"))
summary(glmm_seriola_rivoliana)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_rivoliana
## 
##      AIC      BIC   logLik deviance df.resid 
##   1065.4   1089.2   -528.7   1057.4     2783 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.01212  0.1101  
## Number of obs: 2787, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0968 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.05972    0.02911  -2.052   0.0402 *  
## Spawnyes     0.04774    0.01209   3.949 7.86e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_rivoliana$KUD95 ~ data_seriola_rivoliana$Spawn)

#################################################################################
data_serranus_atricauda <- subset(week_kuds, Species == "Satr")

glmm_serranus_atricauda <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_serranus_atricauda, family = Gamma(link="log"))
summary(glmm_serranus_atricauda)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_atricauda
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3445.6  -3427.7   1726.8  -3453.6      646 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 8.305e-05 0.009113
## Number of obs: 650, groups:  Transmitter, 9
## 
## Dispersion estimate for Gamma family (sigma^2): 0.000474 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.263748   0.003475  -75.89   <2e-16 ***
## Spawnyes    -0.002961   0.001762   -1.68   0.0929 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_atricauda$KUD95 ~ data_serranus_atricauda$Spawn)

#################################################################################
data_serranus_scriba <- subset(week_kuds, Species == "Sscr")

glmm_serranus_scriba <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_serranus_scriba, family = Gamma(link="log"))
summary(glmm_serranus_scriba)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_scriba
## 
##      AIC      BIC   logLik deviance df.resid 
##    -30.5    -25.3     19.3    -38.5       23 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02947  0.1717  
## Number of obs: 27, groups:  Transmitter, 10
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00803 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.116927   0.082518  -1.417    0.156
## Spawnyes    -0.002848   0.063364  -0.045    0.964
boxplot(data_serranus_scriba$KUD95 ~ data_serranus_scriba$Spawn)

#################################################################################
data_solea_senegalensis <- subset(week_kuds, Species == "Ssen")

glmm_solea_senegalensis <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_solea_senegalensis, family = Gamma(link="log"))
summary(glmm_solea_senegalensis)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_solea_senegalensis
## 
##      AIC      BIC   logLik deviance df.resid 
##    -38.6    -24.7     23.3    -46.6      233 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.05953  0.244   
## Number of obs: 237, groups:  Transmitter, 22
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0456 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.08365    0.05788   1.445    0.148
## Spawnyes    -0.03383    0.03830  -0.883    0.377
boxplot(data_solea_senegalensis$KUD95 ~ data_solea_senegalensis$Spawn)

#################################################################################
data_sparisoma_cretense <- subset(week_kuds, Species == "Scre")

glmm_sparisoma_cretense <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sparisoma_cretense, family = Gamma(link="log"))
summary(glmm_sparisoma_cretense)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sparisoma_cretense
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1031.2  -1012.6    519.6  -1039.2      765 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.01394  0.1181  
## Number of obs: 769, groups:  Transmitter, 10
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0195 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -0.117408   0.038447  -3.054  0.00226 **
## Spawnyes    -0.002199   0.010253  -0.214  0.83017   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparisoma_cretense$KUD95 ~ data_sparisoma_cretense$Spawn)

#################################################################################
data_sparus_aurata <- subset(week_kuds, Species == "Saur")

glmm_sparus_aurata <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sparus_aurata, family = Gamma(link="log"))
summary(glmm_sparus_aurata)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata
## 
##      AIC      BIC   logLik deviance df.resid 
##   1418.2   1438.8   -705.1   1410.2     1256 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.3676   0.6063  
## Number of obs: 1260, groups:  Transmitter, 50
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0901 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.48730    0.08823   5.523 3.33e-08 ***
## Spawnyes     0.02017    0.02158   0.935     0.35    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata$KUD95 ~ data_sparus_aurata$Spawn)

#################################################################################
data_sphyraena_viridensis <- subset(week_kuds, Species == "Svir")

glmm_sphyraena_viridensis <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis
## 
##      AIC      BIC   logLik deviance df.resid 
##   2508.2   2530.3  -1250.1   2500.2     1852 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.2697   0.5194  
## Number of obs: 1856, groups:  Transmitter, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.152 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.33384    0.09657   3.457 0.000546 ***
## Spawnyes     0.16774    0.01869   8.974  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sphyraena_viridensis$KUD95 ~ data_sphyraena_viridensis$Spawn)

#################################################################################
data_spondyliosoma_cantharus <- subset(week_kuds, Species == "Scan")

glmm_spondyliosoma_cantharus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_spondyliosoma_cantharus, family = Gamma(link="log"))
summary(glmm_spondyliosoma_cantharus)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_spondyliosoma_cantharus
## 
##      AIC      BIC   logLik deviance df.resid 
##    142.7    160.7    -67.4    134.7      659 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.05021  0.2241  
## Number of obs: 663, groups:  Transmitter, 21
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0657 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.07551    0.05403   1.398  0.16219   
## Spawnyes    -0.05671    0.02129  -2.664  0.00773 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_spondyliosoma_cantharus$KUD95 ~ data_spondyliosoma_cantharus$Spawn)

#################################################################################
#Glmm KUD50 for each Species

data_dentex_dentex <- subset(week_kuds, Species == "Dden")

glmm_dentex_dentex <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dentex_dentex, family = Gamma(link="log"))
summary(glmm_dentex_dentex)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3223.6  -3202.7   1615.8  -3231.6     1373 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.06051  0.246   
## Number of obs: 1377, groups:  Transmitter, 35
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0744 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.37175    0.04412 -31.089  < 2e-16 ***
## Spawnyes     0.05748    0.01527   3.765 0.000166 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex$KUD50 ~ data_dentex_dentex$Spawn)

#################################################################################
data_dicentrarchus_labrax <- subset(week_kuds, Species == "Dlab")

glmm_dicentrarchus_labrax <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax
## 
##      AIC      BIC   logLik deviance df.resid 
##  -2559.1  -2537.9   1283.6  -2567.1     1487 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1448   0.3805  
## Number of obs: 1491, groups:  Transmitter, 121
## 
## Dispersion estimate for Gamma family (sigma^2): 0.123 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.35503    0.03752  -36.12  < 2e-16 ***
## Spawnyes     0.19395    0.02920    6.64  3.1e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax$KUD50 ~ data_dicentrarchus_labrax$Spawn)

#################################################################################
data_diplodus_cervinus <- subset(week_kuds, Species == "Dcer")

glmm_diplodus_cervinus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_cervinus, family = Gamma(link="log"))
summary(glmm_diplodus_cervinus)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_cervinus
## 
##      AIC      BIC   logLik deviance df.resid 
##   -184.9   -174.7     96.4   -192.9       90 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1266   0.3558  
## Number of obs: 94, groups:  Transmitter, 4
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0816 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.28557    0.18486  -6.954 3.55e-12 ***
## Spawnyes    -0.13142    0.06793  -1.935    0.053 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_cervinus$KUD50 ~ data_diplodus_cervinus$Spawn)

#################################################################################
data_diplodus_sargus <- subset(week_kuds, Species == "Dsar")

glmm_diplodus_sargus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus, family = Gamma(link="log"))
summary(glmm_diplodus_sargus)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus
## 
##      AIC      BIC   logLik deviance df.resid 
## -13164.3 -13139.4   6586.1 -13172.3     3704 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.03508  0.1873  
## Number of obs: 3708, groups:  Transmitter, 160
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0352 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.560212   0.016077  -97.05   <2e-16 ***
## Spawnyes     0.009822   0.006760    1.45    0.146    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus$KUD50 ~ data_diplodus_sargus$Spawn)

#################################################################################
data_diplodus_vulgaris <- subset(week_kuds, Species == "Dvul")

glmm_diplodus_vulgaris <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris
## 
##      AIC      BIC   logLik deviance df.resid 
##   -202.3   -194.6    105.1   -210.3       46 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.03668  0.1915  
## Number of obs: 50, groups:  Transmitter, 11
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0166 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.57712    0.06353 -24.826   <2e-16 ***
## Spawnyes    -0.13813    0.06039  -2.287   0.0222 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris$KUD50 ~ data_diplodus_vulgaris$Spawn)

#################################################################################
data_epinephelus_marginatus <- subset(week_kuds, Species == "Emar")

glmm_epinephelus_marginatus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus
## 
##      AIC      BIC   logLik deviance df.resid 
## -15207.9 -15183.8   7607.9 -15215.9     3008 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.03197  0.1788  
## Number of obs: 3012, groups:  Transmitter, 48
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0115 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.731128   0.026115  -66.29  < 2e-16 ***
## Spawnyes     0.028028   0.004024    6.96  3.3e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus$KUD50 ~ data_epinephelus_marginatus$Spawn)

#################################################################################
data_gadus_morhua <- subset(week_kuds, Species == "Gmor")

glmm_gadus_morhua <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_gadus_morhua, family = Gamma(link="log"))
summary(glmm_gadus_morhua)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua
## 
##      AIC      BIC   logLik deviance df.resid 
##  -9650.5  -9626.3   4829.3  -9658.5     3166 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.03984  0.1996  
## Number of obs: 3170, groups:  Transmitter, 145
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0559 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.58936    0.01998  -79.54  < 2e-16 ***
## Spawnyes     0.04594    0.01107    4.15 3.31e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua$KUD50 ~ data_gadus_morhua$Spawn)

#################################################################################
data_labrus_bergylta <- subset(week_kuds, Species == "Lber")

glmm_labrus_bergylta <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_labrus_bergylta, family = Gamma(link="log"))
summary(glmm_labrus_bergylta)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_labrus_bergylta
## 
##      AIC      BIC   logLik deviance df.resid 
##  -5170.0  -5151.2   2589.0  -5178.0      789 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.002124 0.04609 
## Number of obs: 793, groups:  Transmitter, 25
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00238 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.727118   0.009530 -181.23  < 2e-16 ***
## Spawnyes     0.024741   0.003492    7.08 1.39e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_labrus_bergylta$KUD50 ~ data_labrus_bergylta$Spawn)

#################################################################################
data_lichia_amia<- subset(week_kuds, Species == "Lami")

glmm_lichia_amia <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_lichia_amia, family = Gamma(link="log"))
summary(glmm_lichia_amia)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_lichia_amia
## 
##      AIC      BIC   logLik deviance df.resid 
##      5.2     10.5      1.4     -2.8       24 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev. 
##  Transmitter (Intercept) 1.536e-12 1.239e-06
## Number of obs: 28, groups:  Transmitter, 1
## 
## Dispersion estimate for Gamma family (sigma^2): 0.109 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -0.5626     0.1650  -3.409 0.000652 ***
## Spawnyes      0.2794     0.1782   1.567 0.117030    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_lichia_amia$KUD50 ~ data_lichia_amia$Spawn)

#################################################################################
data_pagrus_pagrus <- subset(week_kuds, Species == "Ppag")

glmm_pagrus_pagrus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus
## 
##      AIC      BIC   logLik deviance df.resid 
##  -2313.6  -2295.6   1160.8  -2321.6      650 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1991   0.4462  
## Number of obs: 654, groups:  Transmitter, 25
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0412 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.55372    0.09176 -16.933   <2e-16 ***
## Spawnyes     0.03840    0.01673   2.294   0.0218 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pagrus_pagrus$KUD50 ~ data_pagrus_pagrus$Spawn)

#################################################################################
data_pomatomus_saltatrix <- subset(week_kuds, Species == "Psal")

glmm_pomatomus_saltatrix <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pomatomus_saltatrix, family = Gamma(link="log"))
summary(glmm_pomatomus_saltatrix)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pomatomus_saltatrix
## 
##      AIC      BIC   logLik deviance df.resid 
##     39.4     51.7    -15.7     31.4      155 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.06272  0.2504  
## Number of obs: 159, groups:  Transmitter, 8
## 
## Dispersion estimate for Gamma family (sigma^2): 0.368 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.69788    0.10724  -6.507 7.64e-11 ***
## Spawnyes    -0.06845    0.13018  -0.526    0.599    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pomatomus_saltatrix$KUD50 ~ data_pomatomus_saltatrix$Spawn)

#################################################################################
data_pseudocaranx_dentex <- subset(week_kuds, Species == "Pden")

glmm_pseudocaranx_dentex <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pseudocaranx_dentex, family = Gamma(link="log"))
summary(glmm_pseudocaranx_dentex)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pseudocaranx_dentex
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3844.6  -3823.3   1926.3  -3852.6     1523 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08244  0.2871  
## Number of obs: 1527, groups:  Transmitter, 31
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0955 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.53944    0.05554 -27.717  < 2e-16 ***
## Spawnyes     0.07720    0.01681   4.592  4.4e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pseudocaranx_dentex$KUD50 ~ data_pseudocaranx_dentex$Spawn)

#################################################################################
data_sciaena_umbra <- subset(week_kuds, Species == "Sumb")

glmm_sciaena_umbra <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra, family = Gamma(link="log"))
summary(glmm_sciaena_umbra)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra
## 
##      AIC      BIC   logLik deviance df.resid 
##   -754.1   -742.2    381.0   -762.1      139 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02172  0.1474  
## Number of obs: 143, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00628 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.64844    0.04060   -40.6  < 2e-16 ***
## Spawnyes    -0.04746    0.01826    -2.6  0.00933 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra$KUD50 ~ data_sciaena_umbra$Spawn)

#################################################################################
data_scorpaena_porcus <- subset(week_kuds, Species == "Spor")

glmm_scorpaena_porcus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_scorpaena_porcus, family = Gamma(link="log"))
summary(glmm_scorpaena_porcus)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_porcus
## 
##      AIC      BIC   logLik deviance df.resid 
##   -282.9   -275.1    145.4   -290.9       48 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 0.0004771 0.02184 
## Number of obs: 52, groups:  Transmitter, 13
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00659 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.70114    0.01877  -90.63   <2e-16 ***
## Spawnyes    -0.05518    0.02466   -2.24   0.0253 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_porcus$KUD50 ~ data_scorpaena_porcus$Spawn)

#################################################################################
data_scorpaena_scrofa <- subset(week_kuds, Species == "Sscr")

glmm_scorpaena_scrofa <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -202.8   -194.5    105.4   -210.8       54 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.002972 0.05451 
## Number of obs: 58, groups:  Transmitter, 7
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0017 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.18855    0.02615  -7.210 5.61e-13 ***
## Spawnyes    -0.01773    0.01702  -1.042    0.297    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa$KUD95 ~ data_scorpaena_scrofa$Spawn)

#################################################################################
data_seriola_dumerili <- subset(week_kuds, Species == "Sdum")

glmm_seriola_dumerili <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_seriola_dumerili, family = Gamma(link="log"))
summary(glmm_seriola_dumerili)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_dumerili
## 
##      AIC      BIC   logLik deviance df.resid 
##   -169.4   -153.9     88.7   -177.4      352 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.04089  0.2022  
## Number of obs: 356, groups:  Transmitter, 8
## 
## Dispersion estimate for Gamma family (sigma^2): 0.202 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.02861    0.08266 -12.444   <2e-16 ***
## Spawnyes     0.42232    0.04948   8.535   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_dumerili$KUD50 ~ data_seriola_dumerili$Spawn)

#################################################################################
data_seriola_rivoliana <- subset(week_kuds, Species == "Sriv")

glmm_seriola_rivoliana <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_seriola_rivoliana, family = Gamma(link="log"))
summary(glmm_seriola_rivoliana)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_rivoliana
## 
##      AIC      BIC   logLik deviance df.resid 
##  -9159.6  -9135.8   4583.8  -9167.6     2783 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.006496 0.0806  
## Number of obs: 2787, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0613 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.667018   0.021514  -77.49  < 2e-16 ***
## Spawnyes     0.028314   0.009618    2.94  0.00324 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_rivoliana$KUD50 ~ data_seriola_rivoliana$Spawn)

#################################################################################
data_serranus_atricauda <- subset(week_kuds, Species == "Satr")

glmm_serranus_atricauda <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_serranus_atricauda, family = Gamma(link="log"))
summary(glmm_serranus_atricauda)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_atricauda
## 
##      AIC      BIC   logLik deviance df.resid 
##  -5402.6  -5384.7   2705.3  -5410.6      646 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 9.271e-05 0.009629
## Number of obs: 650, groups:  Transmitter, 9
## 
## Dispersion estimate for Gamma family (sigma^2): 0.000501 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.796127   0.003653  -491.7   <2e-16 ***
## Spawnyes    -0.004663   0.001812    -2.6   0.0101 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_atricauda$KUD50 ~ data_serranus_atricauda$Spawn)

#################################################################################
data_serranus_scriba <- subset(week_kuds, Species == "Sscr")

glmm_serranus_scriba <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_serranus_scriba, family = Gamma(link="log"))
summary(glmm_serranus_scriba)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_scriba
## 
##      AIC      BIC   logLik deviance df.resid 
##   -111.0   -105.8     59.5   -119.0       23 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02159  0.1469  
## Number of obs: 27, groups:  Transmitter, 10
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0111 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.72592    0.08615 -20.033   <2e-16 ***
## Spawnyes     0.06062    0.07423   0.817    0.414    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_scriba$KUD50 ~ data_serranus_scriba$Spawn)

#################################################################################
data_solea_senegalensis <- subset(week_kuds, Species == "Ssen")

glmm_solea_senegalensis <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_solea_senegalensis, family = Gamma(link="log"))
summary(glmm_solea_senegalensis)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_solea_senegalensis
## 
##      AIC      BIC   logLik deviance df.resid 
##   -794.3   -780.5    401.2   -802.3      233 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.04342  0.2084  
## Number of obs: 237, groups:  Transmitter, 22
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0426 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.49352    0.05066 -29.481   <2e-16 ***
## Spawnyes    -0.03657    0.03680  -0.994     0.32    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_solea_senegalensis$KUD50 ~ data_solea_senegalensis$Spawn)

#################################################################################
data_sparisoma_cretense <- subset(week_kuds, Species == "Scre")

glmm_sparisoma_cretense <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sparisoma_cretense, family = Gamma(link="log"))
summary(glmm_sparisoma_cretense)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sparisoma_cretense
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3587.9  -3569.3   1797.9  -3595.9      765 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02709  0.1646  
## Number of obs: 769, groups:  Transmitter, 10
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0153 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.662623   0.052688 -31.556   <2e-16 ***
## Spawnyes     0.004549   0.009085   0.501    0.617    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparisoma_cretense$KUD50 ~ data_sparisoma_cretense$Spawn)

#################################################################################
data_sparus_aurata <- subset(week_kuds, Species == "Saur")

glmm_sparus_aurata <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sparus_aurata, family = Gamma(link="log"))
summary(glmm_sparus_aurata)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata
## 
##      AIC      BIC   logLik deviance df.resid 
##  -2574.4  -2553.8   1291.2  -2582.4     1256 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.3093   0.5561  
## Number of obs: 1260, groups:  Transmitter, 50
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0901 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.12109    0.08132 -13.786   <2e-16 ***
## Spawnyes     0.04739    0.02160   2.194   0.0282 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata$KUD50 ~ data_sparus_aurata$Spawn)

#################################################################################
data_sphyraena_viridensis <- subset(week_kuds, Species == "Svir")

glmm_sphyraena_viridensis <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis
## 
##      AIC      BIC   logLik deviance df.resid 
##  -4302.9  -4280.8   2155.5  -4310.9     1852 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.192    0.4382  
## Number of obs: 1856, groups:  Transmitter, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.103 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.31015    0.08139 -16.097  < 2e-16 ***
## Spawnyes     0.09604    0.01525   6.297 3.04e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sphyraena_viridensis$KUD50 ~ data_sphyraena_viridensis$Spawn)

#################################################################################
data_spondyliosoma_cantharus <- subset(week_kuds, Species == "Scan")

glmm_spondyliosoma_cantharus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_spondyliosoma_cantharus, family = Gamma(link="log"))
summary(glmm_spondyliosoma_cantharus)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_spondyliosoma_cantharus
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1988.4  -1970.4    998.2  -1996.4      659 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.06214  0.2493  
## Number of obs: 663, groups:  Transmitter, 21
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0586 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.46186    0.05876 -24.877  < 2e-16 ***
## Spawnyes    -0.05414    0.02015  -2.687  0.00721 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_spondyliosoma_cantharus$KUD50 ~ data_spondyliosoma_cantharus$Spawn)

#################################################################################
#Glmm KUD50 for each File

data_dentex_dentex1 <- subset(week_kuds, File == "Dentex_dentex1")

glmm_dentex_dentex1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dentex_dentex1, family = Gamma(link="log"))
summary(glmm_dentex_dentex1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex1
## 
##      AIC      BIC   logLik deviance df.resid 
##  -2280.3  -2261.7   1144.1  -2288.3      774 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.04613  0.2148  
## Number of obs: 778, groups:  Transmitter, 19
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0446 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.43928    0.05150 -27.948  < 2e-16 ***
## Spawnyes     0.08437    0.01557   5.418 6.02e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex1$KUD50 ~ data_dentex_dentex1$Spawn)

#################################################################################
data_dentex_dentex2 <- subset(week_kuds, File == "Dentex_dentex2")

glmm_dentex_dentex2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dentex_dentex2, family = Gamma(link="log"))
summary(glmm_dentex_dentex2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex2
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1086.3  -1068.7    547.2  -1094.3      595 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07066  0.2658  
## Number of obs: 599, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.112 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.29424    0.07216 -17.935   <2e-16 ***
## Spawnyes     0.02110    0.02875   0.734    0.463    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex2$KUD50 ~ data_dentex_dentex2$Spawn)

#################################################################################
data_dicentrarchus_labrax1 <- subset(week_kuds, File == "Dicentrarchus_labrax1")

glmm_dicentrarchus_labrax1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax1, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax1
## 
##      AIC      BIC   logLik deviance df.resid 
##  -2194.2  -2175.2   1101.1  -2202.2      850 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07913  0.2813  
## Number of obs: 854, groups:  Transmitter, 93
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0766 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.46528    0.03192  -45.91   <2e-16 ***
## Spawnyes    -0.01360    0.05476   -0.25    0.804    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax1$KUD50 ~ data_dicentrarchus_labrax1$Spawn)

#################################################################################
data_dicentrarchus_labrax2 <- subset(week_kuds, File == "Dicentrarchus_labrax2")

glmm_dicentrarchus_labrax2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax2, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax2
## 
##      AIC      BIC   logLik deviance df.resid 
##   -534.6   -516.8    271.3   -542.6      633 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1997   0.4469  
## Number of obs: 637, groups:  Transmitter, 28
## 
## Dispersion estimate for Gamma family (sigma^2): 0.179 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.00901    0.09191 -10.978  < 2e-16 ***
## Spawnyes     0.20774    0.03891   5.338 9.37e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax2$KUD50 ~ data_dicentrarchus_labrax2$Spawn)

#################################################################################
data_diplodus_cervinus <- subset(week_kuds, File == "Diplodus_cervinus")

glmm_diplodus_cervinus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_cervinus, family = Gamma(link="log"))
summary(glmm_diplodus_cervinus)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_cervinus
## 
##      AIC      BIC   logLik deviance df.resid 
##   -184.9   -174.7     96.4   -192.9       90 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1266   0.3558  
## Number of obs: 94, groups:  Transmitter, 4
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0816 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.28557    0.18486  -6.954 3.55e-12 ***
## Spawnyes    -0.13142    0.06793  -1.935    0.053 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_cervinus$KUD50 ~ data_diplodus_cervinus$Spawn)

#################################################################################
data_diplodus_sargus1 <- subset(week_kuds, File == "Diplodus_sargus1")

glmm_diplodus_sargus1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus1, family = Gamma(link="log"))
summary(glmm_diplodus_sargus1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus1
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1007.6   -992.1    507.8  -1015.6      347 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.06204  0.2491  
## Number of obs: 351, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0617 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.53034    0.06840 -22.372   <2e-16 ***
## Spawnyes     0.00321    0.03104   0.103    0.918    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus1$KUD50 ~ data_diplodus_sargus1$Spawn)

#################################################################################
data_diplodus_sargus2 <- subset(week_kuds, File == "Diplodus_sargus2")

glmm_diplodus_sargus2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus2, family = Gamma(link="log"))
summary(glmm_diplodus_sargus2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus2
## 
##      AIC      BIC   logLik deviance df.resid 
##  -2444.7  -2426.7   1226.4  -2452.7      656 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02645  0.1626  
## Number of obs: 660, groups:  Transmitter, 20
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0333 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.48767    0.04036  -36.86   <2e-16 ***
## Spawnyes    -0.04083    0.01751   -2.33   0.0197 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus2$KUD50 ~ data_diplodus_sargus2$Spawn)

#################################################################################
data_diplodus_sargus3 <- subset(week_kuds, File == "Diplodus_sargus3")

glmm_diplodus_sargus3 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus3, family = Gamma(link="log"))
summary(glmm_diplodus_sargus3)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus3
## 
##      AIC      BIC   logLik deviance df.resid 
##   -403.3   -393.7    205.6   -411.3       76 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 0.0006003 0.0245  
## Number of obs: 80, groups:  Transmitter, 4
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0104 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.76106    0.01966  -89.58  < 2e-16 ***
## Spawnyes     0.08940    0.02304    3.88 0.000104 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus3$KUD50 ~ data_diplodus_sargus3$Spawn)

#################################################################################
data_diplodus_sargus4 <- subset(week_kuds, File == "Diplodus_sargus4")

glmm_diplodus_sargus4 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus4, family = Gamma(link="log"))
summary(glmm_diplodus_sargus4)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus4
## 
##      AIC      BIC   logLik deviance df.resid 
##  -6143.6  -6122.4   3075.8  -6151.6     1470 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.03507  0.1873  
## Number of obs: 1474, groups:  Transmitter, 41
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0185 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.579419   0.030197  -52.30   <2e-16 ***
## Spawnyes     0.001954   0.007205    0.27    0.786    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus4$KUD50 ~ data_diplodus_sargus4$Spawn)

#################################################################################
data_diplodus_sargus5 <- subset(week_kuds, File == "Diplodus_sargus5")

glmm_diplodus_sargus5 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus5, family = Gamma(link="log"))
summary(glmm_diplodus_sargus5)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus5
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3438.5  -3418.4   1723.2  -3446.5     1098 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02855  0.169   
## Number of obs: 1102, groups:  Transmitter, 73
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0532 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.56587    0.02359  -66.39   <2e-16 ***
## Spawnyes     0.03946    0.01575    2.51   0.0122 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus5$KUD50 ~ data_diplodus_sargus5$Spawn)

#################################################################################
data_diplodus_sargus6 <- subset(week_kuds, File == "Diplodus_sargus6")

glmm_diplodus_sargus6 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus6, family = Gamma(link="log"))
summary(glmm_diplodus_sargus6)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus6
## 
##      AIC      BIC   logLik deviance df.resid 
##   -223.1   -216.2    115.5   -231.1       37 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08092  0.2845  
## Number of obs: 41, groups:  Transmitter, 6
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00378 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.60644    0.11761 -13.659   <2e-16 ***
## Spawnyes     0.03602    0.02452   1.469    0.142    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus6$KUD50 ~ data_diplodus_sargus6$Spawn)

#################################################################################
data_diplodus_vulgaris1 <- subset(week_kuds, File == "Diplodus_vulgaris1")

glmm_diplodus_vulgaris1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris1, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -195.6   -188.3    101.8   -203.6       42 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.04232  0.2057  
## Number of obs: 46, groups:  Transmitter, 9
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0133 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.60890    0.07275 -22.115   <2e-16 ***
## Spawnyes    -0.05592    0.06168  -0.907    0.365    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris1$KUD50 ~ data_diplodus_vulgaris1$Spawn)

#################################################################################
data_diplodus_vulgaris2 <- subset(week_kuds, File == "Diplodus_vulgaris2")

glmm_diplodus_vulgaris2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris2, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris2
## 
##      AIC      BIC   logLik deviance df.resid 
##    -26.9    -29.3     17.4    -34.9        0 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.002723 0.05218 
## Number of obs: 4, groups:  Transmitter, 2
## 
## Dispersion estimate for Gamma family (sigma^2): 9.14e-06 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.283298   0.036960  -34.72   <2e-16 ***
## Spawnyes    -0.567608   0.003708 -153.06   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris2$KUD50 ~ data_diplodus_vulgaris2$Spawn)

#################################################################################
data_epinephelus_marginatus1 <- subset(week_kuds, File == "Epinephelus_marginatus1")

glmm_epinephelus_marginatus1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus1, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus1
## 
##      AIC      BIC   logLik deviance df.resid 
## -10604.1 -10581.6   5306.0 -10612.1     2051 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.005139 0.07169 
## Number of obs: 2055, groups:  Transmitter, 11
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0109 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.767219   0.021966  -80.45  < 2e-16 ***
## Spawnyes     0.020935   0.004637    4.51 6.34e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus1$KUD50 ~ data_epinephelus_marginatus1$Spawn)

#################################################################################
data_epinephelus_marginatus2 <- subset(week_kuds, File == "Epinephelus_marginatus2")

glmm_epinephelus_marginatus2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus2, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus2
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3693.3  -3677.0   1850.6  -3701.3      433 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 4.253e-05 0.006522
## Number of obs: 437, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.000423 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.794331   0.002075  -864.6  < 2e-16 ***
## Spawnyes     0.007375   0.002189     3.4 0.000755 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus2$KUD50 ~ data_epinephelus_marginatus2$Spawn)

#################################################################################
data_epinephelus_marginatus3 <- subset(week_kuds, File == "Epinephelus_marginatus3")

glmm_epinephelus_marginatus3 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus3, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus3)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus3
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1368.5  -1354.8    688.2  -1376.5      223 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 0.0002907 0.01705 
## Number of obs: 227, groups:  Transmitter, 4
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00461 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.785956   0.010985 -162.58  < 2e-16 ***
## Spawnyes     0.023495   0.009119    2.58  0.00999 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus3$KUD50 ~ data_epinephelus_marginatus3$Spawn)

#################################################################################
data_epinephelus_marginatus4 <- subset(week_kuds, File == "Epinephelus_marginatus4")

glmm_epinephelus_marginatus4 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus4, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus4)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus4
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1033.4  -1018.7    520.7  -1041.4      289 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.06315  0.2513  
## Number of obs: 293, groups:  Transmitter, 17
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0367 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.67263    0.06362  -26.29  < 2e-16 ***
## Spawnyes     0.11494    0.02389    4.81 1.51e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus4$KUD50 ~ data_epinephelus_marginatus4$Spawn)

#################################################################################
data_gadus_morhua1 <- subset(week_kuds, File == "Gadus_morhua1")

glmm_gadus_morhua1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_gadus_morhua1, family = Gamma(link="log"))
summary(glmm_gadus_morhua1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua1
## 
##      AIC      BIC   logLik deviance df.resid 
##  -4354.4  -4332.8   2181.2  -4362.4     1631 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.04348  0.2085  
## Number of obs: 1635, groups:  Transmitter, 60
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0697 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.51844    0.03117  -48.72  < 2e-16 ***
## Spawnyes     0.08095    0.01598    5.07 4.08e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua1$KUD50 ~ data_gadus_morhua1$Spawn)

#################################################################################
data_gadus_morhua2 <- subset(week_kuds, File == "Gadus_morhua2")

glmm_gadus_morhua2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_gadus_morhua2, family = Gamma(link="log"))
summary(glmm_gadus_morhua2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua2
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3787.9  -3767.8   1898.0  -3795.9     1132 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.03061  0.175   
## Number of obs: 1136, groups:  Transmitter, 56
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0486 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.57239    0.03120  -50.40   <2e-16 ***
## Spawnyes    -0.04405    0.02104   -2.09   0.0363 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua2$KUD50 ~ data_gadus_morhua2$Spawn)

#################################################################################
data_gadus_morhua3 <- subset(week_kuds, File == "Gadus_morhua3")

glmm_gadus_morhua3 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_gadus_morhua3, family = Gamma(link="log"))
summary(glmm_gadus_morhua3)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua3
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1819.0  -1803.0    913.5  -1827.0      395 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.008528 0.09235 
## Number of obs: 399, groups:  Transmitter, 29
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0158 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.70272    0.02317  -73.47  < 2e-16 ***
## Spawnyes     0.05523    0.01494    3.70 0.000219 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua3$KUD50 ~ data_gadus_morhua3$Spawn)

#################################################################################
data_labrus_bergylta <- subset(week_kuds, File == "Labrus_bergylta")

glmm_labrus_bergylta <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_labrus_bergylta, family = Gamma(link="log"))
summary(glmm_labrus_bergylta)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_labrus_bergylta
## 
##      AIC      BIC   logLik deviance df.resid 
##  -5170.0  -5151.2   2589.0  -5178.0      789 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.002124 0.04609 
## Number of obs: 793, groups:  Transmitter, 25
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00238 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.727118   0.009530 -181.23  < 2e-16 ***
## Spawnyes     0.024741   0.003492    7.08 1.39e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_labrus_bergylta$KUD50 ~ data_labrus_bergylta$Spawn)

#################################################################################
data_lichia_amia<- subset(week_kuds, File == "Lichia_amia")

glmm_lichia_amia <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_lichia_amia, family = Gamma(link="log"))
summary(glmm_lichia_amia)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_lichia_amia
## 
##      AIC      BIC   logLik deviance df.resid 
##      5.2     10.5      1.4     -2.8       24 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev. 
##  Transmitter (Intercept) 1.536e-12 1.239e-06
## Number of obs: 28, groups:  Transmitter, 1
## 
## Dispersion estimate for Gamma family (sigma^2): 0.109 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -0.5626     0.1650  -3.409 0.000652 ***
## Spawnyes      0.2794     0.1782   1.567 0.117030    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_lichia_amia$KUD50 ~ data_lichia_amia$Spawn)

#################################################################################
data_pagrus_pagrus1 <- subset(week_kuds, File == "Pagrus_pagrus1")

glmm_pagrus_pagrus1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus1, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus1
## 
##      AIC      BIC   logLik deviance df.resid 
##  -2241.5  -2223.8   1124.8  -2249.5      614 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02536  0.1593  
## Number of obs: 618, groups:  Transmitter, 20
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0413 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.66357    0.03941  -42.22   <2e-16 ***
## Spawnyes     0.03767    0.01717    2.19   0.0283 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pagrus_pagrus1$KUD50 ~ data_pagrus_pagrus1$Spawn)

#################################################################################
data_pagrus_pagrus2 <- subset(week_kuds, File == "Pagrus_pagrus2")

glmm_pagrus_pagrus2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus2, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus2
## 
##      AIC      BIC   logLik deviance df.resid 
##    -97.9    -91.6     53.0   -105.9       32 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.7335   0.8565  
## Number of obs: 36, groups:  Transmitter, 5
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0373 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -1.083803   0.390730  -2.774  0.00554 **
## Spawnyes     0.007209   0.068040   0.106  0.91562   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pagrus_pagrus2$KUD50 ~ data_pagrus_pagrus2$Spawn)

#################################################################################
data_pomatomus_saltatrix <- subset(week_kuds, File == "Pomatomus_saltatrix")

glmm_pomatomus_saltatrix <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pomatomus_saltatrix, family = Gamma(link="log"))
summary(glmm_pomatomus_saltatrix)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pomatomus_saltatrix
## 
##      AIC      BIC   logLik deviance df.resid 
##     39.4     51.7    -15.7     31.4      155 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.06272  0.2504  
## Number of obs: 159, groups:  Transmitter, 8
## 
## Dispersion estimate for Gamma family (sigma^2): 0.368 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.69788    0.10724  -6.507 7.64e-11 ***
## Spawnyes    -0.06845    0.13018  -0.526    0.599    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pomatomus_saltatrix$KUD50 ~ data_pomatomus_saltatrix$Spawn)

#################################################################################
data_pseudocaranx_dentex <- subset(week_kuds, File == "Pseudocaranx_dentex")

glmm_pseudocaranx_dentex <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pseudocaranx_dentex, family = Gamma(link="log"))
summary(glmm_pseudocaranx_dentex)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pseudocaranx_dentex
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3844.6  -3823.3   1926.3  -3852.6     1523 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08244  0.2871  
## Number of obs: 1527, groups:  Transmitter, 31
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0955 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.53944    0.05554 -27.717  < 2e-16 ***
## Spawnyes     0.07720    0.01681   4.592  4.4e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pseudocaranx_dentex$KUD50 ~ data_pseudocaranx_dentex$Spawn)

#################################################################################
data_sciaena_umbra1 <- subset(week_kuds, File == "Sciaena_umbra1")

glmm_sciaena_umbra1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra1, family = Gamma(link="log"))
summary(glmm_sciaena_umbra1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -889.6   -878.1    448.8   -897.6      125 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.0109   0.1044  
## Number of obs: 129, groups:  Transmitter, 15
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00121 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.704016   0.028254  -60.31   <2e-16 ***
## Spawnyes    -0.010727   0.009007   -1.19    0.234    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra1$KUD50 ~ data_sciaena_umbra1$Spawn)

#################################################################################
data_sciaena_umbra2 <- subset(week_kuds, File == "Sciaena_umbra2")

glmm_sciaena_umbra2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra2, family = Gamma(link="log"))
summary(glmm_sciaena_umbra2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra2
## 
##      AIC      BIC   logLik deviance df.resid 
##    -33.4    -30.8     20.7    -41.4       10 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev. 
##  Transmitter (Intercept) 1.673e-11 4.091e-06
## Number of obs: 14, groups:  Transmitter, 1
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0396 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.16613    0.07522  -15.50   <2e-16 ***
## Spawnyes    -0.20429    0.10638   -1.92   0.0548 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra2$KUD50 ~ data_sciaena_umbra2$Spawn)

#################################################################################
data_scorpaena_porcus <- subset(week_kuds, File == "Scorpaena_porcus")

glmm_scorpaena_porcus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_scorpaena_porcus, family = Gamma(link="log"))
summary(glmm_scorpaena_porcus)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_porcus
## 
##      AIC      BIC   logLik deviance df.resid 
##   -282.9   -275.1    145.4   -290.9       48 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 0.0004771 0.02184 
## Number of obs: 52, groups:  Transmitter, 13
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00659 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.70114    0.01877  -90.63   <2e-16 ***
## Spawnyes    -0.05518    0.02466   -2.24   0.0253 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_porcus$KUD50 ~ data_scorpaena_porcus$Spawn)

#################################################################################
data_scorpaena_scrofa1 <- subset(week_kuds, File == "Scorpaena_scrofa1")

glmm_scorpaena_scrofa1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa1, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -363.4   -355.2    185.7   -371.4       54 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.003692 0.06077 
## Number of obs: 58, groups:  Transmitter, 7
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00229 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.71056    0.02962  -57.75   <2e-16 ***
## Spawnyes    -0.02598    0.01977   -1.31    0.189    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa1$KUD50 ~ data_scorpaena_scrofa1$Spawn)

#################################################################################
data_scorpaena_scrofa2 <- subset(week_kuds, File == "Scorpaena_scrofa2")

glmm_scorpaena_scrofa2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa2, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa2
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1820.4  -1803.5    914.2  -1828.4      504 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02351  0.1533  
## Number of obs: 508, groups:  Transmitter, 11
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0371 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.74615    0.04910  -35.57   <2e-16 ***
## Spawnyes     0.16387    0.01782    9.20   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa2$KUD50 ~ data_scorpaena_scrofa2$Spawn)

#################################################################################
data_seriola_dumerili <- subset(week_kuds, File == "Seriola_dumerili")

glmm_seriola_dumerili <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_seriola_dumerili, family = Gamma(link="log"))
summary(glmm_seriola_dumerili)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_dumerili
## 
##      AIC      BIC   logLik deviance df.resid 
##   -169.4   -153.9     88.7   -177.4      352 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.04089  0.2022  
## Number of obs: 356, groups:  Transmitter, 8
## 
## Dispersion estimate for Gamma family (sigma^2): 0.202 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.02861    0.08266 -12.444   <2e-16 ***
## Spawnyes     0.42232    0.04948   8.535   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_dumerili$KUD50 ~ data_seriola_dumerili$Spawn)

#################################################################################
data_seriola_rivoliana <- subset(week_kuds, File == "Seriola_rivoliana")

glmm_seriola_rivoliana <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_seriola_rivoliana, family = Gamma(link="log"))
summary(glmm_seriola_rivoliana)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_rivoliana
## 
##      AIC      BIC   logLik deviance df.resid 
##  -9159.6  -9135.8   4583.8  -9167.6     2783 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.006496 0.0806  
## Number of obs: 2787, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0613 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.667018   0.021514  -77.49  < 2e-16 ***
## Spawnyes     0.028314   0.009618    2.94  0.00324 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_rivoliana$KUD50 ~ data_seriola_rivoliana$Spawn)

#################################################################################
data_serranus_atricauda <- subset(week_kuds, File == "Serranus_atricauda")

glmm_serranus_atricauda <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_serranus_atricauda, family = Gamma(link="log"))
summary(glmm_serranus_atricauda)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_atricauda
## 
##      AIC      BIC   logLik deviance df.resid 
##  -5402.6  -5384.7   2705.3  -5410.6      646 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 9.271e-05 0.009629
## Number of obs: 650, groups:  Transmitter, 9
## 
## Dispersion estimate for Gamma family (sigma^2): 0.000501 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.796127   0.003653  -491.7   <2e-16 ***
## Spawnyes    -0.004663   0.001812    -2.6   0.0101 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_atricauda$KUD50 ~ data_serranus_atricauda$Spawn)

#################################################################################
data_serranus_scriba <- subset(week_kuds, File == "Serranus_scriba")

glmm_serranus_scriba <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_serranus_scriba, family = Gamma(link="log"))
summary(glmm_serranus_scriba)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_scriba
## 
##      AIC      BIC   logLik deviance df.resid 
##   -111.0   -105.8     59.5   -119.0       23 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02159  0.1469  
## Number of obs: 27, groups:  Transmitter, 10
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0111 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.72592    0.08615 -20.033   <2e-16 ***
## Spawnyes     0.06062    0.07423   0.817    0.414    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_scriba$KUD50 ~ data_serranus_scriba$Spawn)

#################################################################################
data_solea_senegalensis <- subset(week_kuds, File == "Solea_senegalensis")

glmm_solea_senegalensis <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_solea_senegalensis, family = Gamma(link="log"))
summary(glmm_solea_senegalensis)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_solea_senegalensis
## 
##      AIC      BIC   logLik deviance df.resid 
##   -794.3   -780.5    401.2   -802.3      233 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.04342  0.2084  
## Number of obs: 237, groups:  Transmitter, 22
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0426 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.49352    0.05066 -29.481   <2e-16 ***
## Spawnyes    -0.03657    0.03680  -0.994     0.32    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_solea_senegalensis$KUD50 ~ data_solea_senegalensis$Spawn)

#################################################################################
data_sparisoma_cretense <- subset(week_kuds, File == "Sparisoma_cretense")

glmm_sparisoma_cretense <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sparisoma_cretense, family = Gamma(link="log"))
summary(glmm_sparisoma_cretense)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sparisoma_cretense
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3587.9  -3569.3   1797.9  -3595.9      765 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02709  0.1646  
## Number of obs: 769, groups:  Transmitter, 10
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0153 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.662623   0.052688 -31.556   <2e-16 ***
## Spawnyes     0.004549   0.009085   0.501    0.617    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparisoma_cretense$KUD50 ~ data_sparisoma_cretense$Spawn)

#################################################################################
data_sparus_aurata1 <- subset(week_kuds, File == "Sparus_aurata1")

glmm_sparus_aurata1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sparus_aurata1, family = Gamma(link="log"))
summary(glmm_sparus_aurata1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -854.6   -843.2    431.3   -862.6      123 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.001468 0.03831 
## Number of obs: 127, groups:  Transmitter, 7
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00199 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.768618   0.016448 -107.53   <2e-16 ***
## Spawnyes     0.003994   0.009427    0.42    0.672    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata1$KUD50 ~ data_sparus_aurata1$Spawn)

#################################################################################
data_sparus_aurata2 <- subset(week_kuds, File == "Sparus_aurata2")

glmm_sparus_aurata2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sparus_aurata2, family = Gamma(link="log"))
summary(glmm_sparus_aurata2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata2
## 
##      AIC      BIC   logLik deviance df.resid 
##  -2100.7  -2080.6   1054.3  -2108.7     1129 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.2786   0.5279  
## Number of obs: 1133, groups:  Transmitter, 43
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0996 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.01828    0.08383 -12.147   <2e-16 ***
## Spawnyes     0.05459    0.02412   2.264   0.0236 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata2$KUD50 ~ data_sparus_aurata2$Spawn)

#################################################################################
data_sphyraena_viridensis1 <- subset(week_kuds, File == "Sphyraena_viridensis1")

glmm_sphyraena_viridensis1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis1, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis1
## 
##      AIC      BIC   logLik deviance df.resid 
##  -4622.5  -4601.8   2315.2  -4630.5     1294 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.009475 0.09734 
## Number of obs: 1298, groups:  Transmitter, 13
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0478 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.68092    0.02922  -57.53   <2e-16 ***
## Spawnyes     0.02217    0.01240    1.79   0.0739 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sphyraena_viridensis1$KUD50 ~ data_sphyraena_viridensis1$Spawn)

#################################################################################
data_sphyraena_viridensis2 <- subset(week_kuds, File == "Sphyraena_viridensis2")

glmm_sphyraena_viridensis2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis2, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis2
## 
##      AIC      BIC   logLik deviance df.resid 
##   -292.5   -275.2    150.2   -300.5      554 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.09615  0.3101  
## Number of obs: 558, groups:  Transmitter, 17
## 
## Dispersion estimate for Gamma family (sigma^2): 0.219 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.06979    0.08232 -12.995  < 2e-16 ***
## Spawnyes     0.27259    0.04106   6.639 3.16e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sphyraena_viridensis2$KUD50 ~ data_sphyraena_viridensis2$Spawn)

#################################################################################
data_spondyliosoma_cantharus <- subset(week_kuds, File == "Spondyliosoma_cantharus")

glmm_spondyliosoma_cantharus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_spondyliosoma_cantharus, family = Gamma(link="log"))
summary(glmm_spondyliosoma_cantharus)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_spondyliosoma_cantharus
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1988.4  -1970.4    998.2  -1996.4      659 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.06214  0.2493  
## Number of obs: 663, groups:  Transmitter, 21
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0586 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.46186    0.05876 -24.877  < 2e-16 ***
## Spawnyes    -0.05414    0.02015  -2.687  0.00721 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_spondyliosoma_cantharus$KUD50 ~ data_spondyliosoma_cantharus$Spawn)

#################################################################################
#Spawn
lm_spawn <- lm(KUD95 ~ Spawn, data=week_kuds)
glm_spawn <- glm(KUD95 ~ Spawn, data=week_kuds, family=Gamma(link="log"))
gam_spawn <- gam(KUD95 ~ Spawn, data=week_kuds, family=Gamma(link="log"))
glmmF_spawn <- glmmTMB(KUD95 ~ Spawn + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_spawn <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_spawn <- glmmTMB(KUD95 ~ Spawn + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_spawn <- gamm(KUD95 ~ Spawn, random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_spawn <- gamm4(KUD95 ~ Spawn, random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_spawn <- gamm(KUD95 ~ Spawn, random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
## iteration 5
## iteration 6
## iteration 7
#gamm4T_spawn <- gamm4(KUD95 ~ Spawn, random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_spawn <- gamm(KUD95 ~ Spawn, random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
## iteration 5
#gamm4S_spawn <- gamm4(KUD95 ~ Spawn, random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))

AIC(lm_spawn, glm_spawn, gam_spawn, glmmF_spawn, glmmT_spawn, glmmS_spawn)
##             df      AIC
## lm_spawn     3 60419.37
## glm_spawn    3 35381.60
## gam_spawn    3 42803.78
## glmmF_spawn  4 20393.15
## glmmT_spawn  4 10135.23
## glmmS_spawn  4 25459.97
summary(gammF_spawn$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   30561.22 30593.82 -15276.61
## 
## Random effects:
##  Formula: ~1 | File
##         (Intercept)  Residual
## StdDev:   0.3692995 0.4372845
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                   Value Std.Error    DF  t-value p-value
## X(Intercept) 0.08808436 0.0542129 25563 1.624786  0.1042
## XSpawnyes    0.04672129 0.0058480 25563 7.989281  0.0000
##  Correlation: 
##           X(Int)
## XSpawnyes -0.056
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7294488 -0.5107414 -0.2007781  0.1655619 18.8074454 
## 
## Number of Observations: 25612
## Number of Groups: 48
summary(gammT_spawn$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   16203.53 16236.13 -8097.763
## 
## Random effects:
##  Formula: ~1 | Transmitter
##         (Intercept)  Residual
## StdDev:   0.3813896 0.3148426
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                   Value   Std.Error    DF   t-value p-value
## X(Intercept) 0.06814541 0.013832668 24761  4.926411       0
## XSpawnyes    0.05285775 0.004361867 24761 12.118149       0
##  Correlation: 
##           X(Int)
## XSpawnyes -0.17 
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -2.4112885 -0.4456240 -0.1179616  0.1086597 16.9319189 
## 
## Number of Observations: 25612
## Number of Groups: 850
summary(gammS_spawn$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   38606.74 38639.35 -19299.37
## 
## Random effects:
##  Formula: ~1 | Species
##         (Intercept)  Residual
## StdDev:   0.3844374 0.5125622
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                   Value  Std.Error    DF  t-value p-value
## X(Intercept) 0.12112127 0.07162379 25581 1.691076  0.0908
## XSpawnyes    0.06544614 0.00677943 25581 9.653640  0.0000
##  Correlation: 
##           X(Int)
## XSpawnyes -0.049
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.4817810 -0.4586271 -0.2083884  0.1350119 17.0458995 
## 
## Number of Observations: 25612
## Number of Groups: 30
#Spawn
lm_spawn1 <- lm(KUD50 ~ Spawn, data=week_kuds)
glm_spawn1 <- glm(KUD50 ~ Spawn, data=week_kuds, family=Gamma(link="log"))
gam_spawn1 <- gam(KUD50 ~ Spawn, data=week_kuds, family=Gamma(link="log"))
glmmF_spawn1 <- glmmTMB(KUD50 ~ Spawn + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_spawn1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_spawn1 <- glmmTMB(KUD50 ~ Spawn + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_spawn1 <- gamm(KUD50 ~ Spawn, random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_spawn1 <- gamm4(KUD50 ~ Spawn, random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_spawn1 <- gamm(KUD50 ~ Spawn, random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4T_spawn1 <- gamm4(KUD50 ~ Spawn, random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_spawn1 <- gamm(KUD50 ~ Spawn, random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4S_spawn1 <- gamm4(KUD50 ~ Spawn, random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))

AIC(lm_spawn1, glm_spawn1, gam_spawn1, glmmF_spawn1, glmmT_spawn1, glmmS_spawn1)
##              df       AIC
## lm_spawn1     3 -31827.02
## glm_spawn1    3 -52600.77
## gam_spawn1    3 -47250.51
## glmmF_spawn1  4 -65918.50
## glmmT_spawn1  4 -76088.67
## glmmS_spawn1  4 -61271.32
summary(gammF_spawn1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   23069.61 23102.22 -11530.81
## 
## Random effects:
##  Formula: ~1 | File
##         (Intercept)  Residual
## StdDev:   0.3168224 0.3777924
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                   Value  Std.Error    DF   t-value p-value
## X(Intercept) -1.5028836 0.04651849 25563 -32.30723       0
## XSpawnyes     0.0399475 0.00505236 25563   7.90671       0
##  Correlation: 
##           X(Int)
## XSpawnyes -0.056
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7968283 -0.4771821 -0.2368402  0.1254108 25.1725237 
## 
## Number of Observations: 25612
## Number of Groups: 48
summary(gammT_spawn1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   10048.81 10081.42 -5020.407
## 
## Random effects:
##  Formula: ~1 | Transmitter
##         (Intercept)  Residual
## StdDev:    0.335805 0.2792604
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                   Value   Std.Error    DF    t-value p-value
## X(Intercept) -1.5089937 0.012187944 24761 -123.81036       0
## XSpawnyes     0.0438886 0.003868605 24761   11.34481       0
##  Correlation: 
##           X(Int)
## XSpawnyes -0.171
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.57124862 -0.40877404 -0.12324516  0.09659081 17.11076084 
## 
## Number of Observations: 25612
## Number of Groups: 850
summary(gammS_spawn1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##       AIC     BIC    logLik
##   30096.7 30129.3 -15044.35
## 
## Random effects:
##  Formula: ~1 | Species
##         (Intercept)  Residual
## StdDev:   0.3326315 0.4340953
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                   Value  Std.Error    DF    t-value p-value
## X(Intercept) -1.4739659 0.06193078 25581 -23.800214       0
## XSpawnyes     0.0499525 0.00574165 25581   8.700025       0
##  Correlation: 
##           X(Int)
## XSpawnyes -0.048
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.5690784 -0.4951357 -0.2216829  0.1296522 23.1223745 
## 
## Number of Observations: 25612
## Number of Groups: 30
glmmT_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))

glmmF_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|File), data=week_kuds, family=Gamma(link="log"))

glmmS_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Species), data=week_kuds, family=Gamma(link="log"))

glmmTF_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log"))

glmmTS_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log"))

glmmFS_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|File) + (1|Species), data=week_kuds, family=Gamma(link="log"))

glmmTFS_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File) + (1|Species), data=week_kuds, family=Gamma(link="log"))


AIC(glmmT_total, glmmF_total, glmmS_total, glmmTF_total, glmmTS_total, glmmFS_total, glmmTFS_total)
##               df       AIC
## glmmT_total   16  9783.083
## glmmF_total   16 20264.812
## glmmS_total   16 22364.422
## glmmTF_total  17  9638.072
## glmmTS_total  17  9727.926
## glmmFS_total  17 20266.812
## glmmTFS_total 18  9640.072
#Now we need to compare if the models are statistically different or not
anova(glmmT_total, glmmTF_total) #significant differences, this two models differ statistically
## Data: week_kuds
## Models:
## glmmT_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmT_total:     Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmT_total:     MonitArea_km2 + (1 | Transmitter), zi=~0, disp=~1
## glmmTF_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total:     Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total:     MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
##              Df    AIC    BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)    
## glmmT_total  16 9783.1 9913.5 -4875.5   9751.1                             
## glmmTF_total 17 9638.1 9776.6 -4802.0   9604.1 147.01      1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(glmmT_total, glmmTFS_total) #significant differences, this two models differ statistically
## Data: week_kuds
## Models:
## glmmT_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmT_total:     Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmT_total:     MonitArea_km2 + (1 | Transmitter), zi=~0, disp=~1
## glmmTFS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total:     Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total:     MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
##               Df    AIC    BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)    
## glmmT_total   16 9783.1 9913.5 -4875.5   9751.1                             
## glmmTFS_total 18 9640.1 9786.8 -4802.0   9604.1 147.01      2  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(glmmTF_total, glmmTFS_total) #non significant differences, this two models do not differ statistically
## Data: week_kuds
## Models:
## glmmTF_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total:     Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total:     MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## glmmTFS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total:     Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total:     MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
##               Df    AIC    BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmTF_total  17 9638.1 9776.6  -4802   9604.1                        
## glmmTFS_total 18 9640.1 9786.8  -4802   9604.1     0      1          1
anova(glmmTF_total, glmmTS_total) #non significant differences, this two models do not differ statistically
## Data: week_kuds
## Models:
## glmmTF_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total:     Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total:     MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## glmmTS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTS_total:     Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmTS_total:     MonitArea_km2 + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
##              Df    AIC    BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmTF_total 17 9638.1 9776.6  -4802   9604.1                        
## glmmTS_total 17 9727.9 9866.5  -4847   9693.9     0      0          1
anova(glmmTS_total, glmmTFS_total) #significant differences, this two models differ statistically
## Data: week_kuds
## Models:
## glmmTS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTS_total:     Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmTS_total:     MonitArea_km2 + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
## glmmTFS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total:     Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total:     MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
##               Df    AIC    BIC logLik deviance  Chisq Chi Df Pr(>Chisq)    
## glmmTS_total  17 9727.9 9866.5  -4847   9693.9                             
## glmmTFS_total 18 9640.1 9786.8  -4802   9604.1 89.853      1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#choose the simplest model with the lower AIC, that is statistically different from the others, which means the model with Transmitter and File as random effect (glmmTF_total)
#Backward elimination KUD95

Total1 <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1)
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability +  
##     Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity +  
##     MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9727.9   9866.5  -4847.0   9693.9    25595 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08341  0.2888  
##  Species     (Intercept) 0.02107  0.1452  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0746 
## 
## Conditional model:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -0.1642530  0.3612144  -0.455  0.64931    
## LengthStd               0.2888442  0.1570213   1.840  0.06584 .  
## BodyMassStd            -0.0658556  0.1148029  -0.574  0.56621    
## Longevity              -0.0019039  0.0035062  -0.543  0.58712    
## Vulnerability          -0.0052428  0.0042805  -1.225  0.22065    
## Troph                   0.0690349  0.1103588   0.626  0.53161    
## Habitatdemersal        -0.0869273  0.1010824  -0.860  0.38981    
## Habitatpelagic-neritic  0.4101459  0.1583945   2.589  0.00961 ** 
## Migrationoceanodromous  0.1165872  0.1269472   0.918  0.35841    
## ComImportmedium        -0.1628333  0.0729114  -2.233  0.02553 *  
## ComImportminor         -0.2017707  0.1157973  -1.742  0.08143 .  
## Spawnyes                0.0565985  0.0038023  14.885  < 2e-16 ***
## ReceiverDensity         0.0003943  0.0006242   0.632  0.52753    
## MonitArea_km2           0.0293867  0.0036892   7.966 1.64e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total2 <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total2)
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ LengthStd + BodyMassStd + Vulnerability + Troph + Habitat +  
##     Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 +  
##     (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9726.2   9856.6  -4847.1   9694.2    25596 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08341  0.2888  
##  Species     (Intercept) 0.02140  0.1463  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0746 
## 
## Conditional model:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -0.1214068  0.3544989  -0.342   0.7320    
## LengthStd               0.2927254  0.1571009   1.863   0.0624 .  
## BodyMassStd            -0.0655384  0.1151142  -0.569   0.5691    
## Vulnerability          -0.0061194  0.0039752  -1.539   0.1237    
## Troph                   0.0594497  0.1096572   0.542   0.5877    
## Habitatdemersal        -0.0883555  0.1015518  -0.870   0.3843    
## Habitatpelagic-neritic  0.4509230  0.1411373   3.195   0.0014 ** 
## Migrationoceanodromous  0.1104295  0.1272993   0.867   0.3857    
## ComImportmedium        -0.1636451  0.0733427  -2.231   0.0257 *  
## ComImportminor         -0.2003437  0.1163942  -1.721   0.0852 .  
## Spawnyes                0.0566086  0.0038023  14.888  < 2e-16 ***
## ReceiverDensity         0.0003934  0.0006253   0.629   0.5293    
## MonitArea_km2           0.0292097  0.0036805   7.936 2.08e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total3 <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Vulnerability + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total3)
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ LengthStd + BodyMassStd + Vulnerability + Habitat + Migration +  
##     ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1 |  
##     Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9724.5   9846.8  -4847.3   9694.5    25597 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08334  0.2887  
##  Species     (Intercept) 0.02199  0.1483  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0746 
## 
## Conditional model:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             0.0355433  0.2044767   0.174   0.8620    
## LengthStd               0.2902111  0.1573619   1.844   0.0652 .  
## BodyMassStd            -0.0670915  0.1154620  -0.581   0.5612    
## Vulnerability          -0.0048255  0.0032259  -1.496   0.1347    
## Habitatdemersal        -0.1015890  0.0995876  -1.020   0.3077    
## Habitatpelagic-neritic  0.4939074  0.1187273   4.160 3.18e-05 ***
## Migrationoceanodromous  0.0890390  0.1224667   0.727   0.4672    
## ComImportmedium        -0.1615100  0.0740094  -2.182   0.0291 *  
## ComImportminor         -0.1821537  0.1125278  -1.619   0.1055    
## Spawnyes                0.0566205  0.0038022  14.891  < 2e-16 ***
## ReceiverDensity         0.0004378  0.0006212   0.705   0.4810    
## MonitArea_km2           0.0294025  0.0036688   8.014 1.11e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total4 <- glmmTMB(KUD95 ~ LengthStd + Vulnerability + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total4)
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ LengthStd + Vulnerability + Habitat + Migration + ComImport +  
##     Spawn + ReceiverDensity + MonitArea_km2 + (1 | Transmitter) +  
##     (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9722.8   9837.0  -4847.4   9694.8    25598 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08333  0.2887  
##  Species     (Intercept) 0.02270  0.1507  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0746 
## 
## Conditional model:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             0.0305347  0.2064582   0.148   0.8824    
## LengthStd               0.2237949  0.1078115   2.076   0.0379 *  
## Vulnerability          -0.0045419  0.0032282  -1.407   0.1594    
## Habitatdemersal        -0.0904350  0.0988607  -0.915   0.3603    
## Habitatpelagic-neritic  0.4987129  0.1199140   4.159  3.2e-05 ***
## Migrationoceanodromous  0.0927178  0.1239680   0.748   0.4545    
## ComImportmedium        -0.1679172  0.0741210  -2.265   0.0235 *  
## ComImportminor         -0.1920240  0.1124932  -1.707   0.0878 .  
## Spawnyes                0.0566271  0.0038022  14.893  < 2e-16 ***
## ReceiverDensity         0.0004720  0.0006207   0.760   0.4470    
## MonitArea_km2           0.0297812  0.0036199   8.227  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total5 <- glmmTMB(KUD95 ~ LengthStd + Vulnerability + Habitat + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total5)
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ LengthStd + Vulnerability + Habitat + ComImport + Spawn +  
##     ReceiverDensity + MonitArea_km2 + (1 | Transmitter) + (1 |      Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9721.4   9827.4  -4847.7   9695.4    25599 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08331  0.2886  
##  Species     (Intercept) 0.02366  0.1538  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0746 
## 
## Conditional model:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             0.0252828  0.2094189   0.121   0.9039    
## LengthStd               0.2193664  0.1079018   2.033   0.0421 *  
## Vulnerability          -0.0036527  0.0030565  -1.195   0.2321    
## Habitatdemersal        -0.1325001  0.0828190  -1.600   0.1096    
## Habitatpelagic-neritic  0.5402646  0.1086736   4.971 6.65e-07 ***
## ComImportmedium        -0.1670076  0.0753550  -2.216   0.0267 *  
## ComImportminor         -0.1810782  0.1132225  -1.599   0.1098    
## Spawnyes                0.0566394  0.0038022  14.896  < 2e-16 ***
## ReceiverDensity         0.0004970  0.0006228   0.798   0.4249    
## MonitArea_km2           0.0297754  0.0036326   8.197 2.47e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total6 <- glmmTMB(KUD95 ~ LengthStd + Vulnerability + Habitat + ComImport + Spawn + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total6)
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ LengthStd + Vulnerability + Habitat + ComImport + Spawn +  
##     MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9720.0   9817.8  -4848.0   9696.0    25600 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08345  0.2889  
##  Species     (Intercept) 0.02377  0.1542  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0746 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             0.046566   0.207978   0.224   0.8228    
## LengthStd               0.214935   0.107669   1.996   0.0459 *  
## Vulnerability          -0.003580   0.003061  -1.170   0.2422    
## Habitatdemersal        -0.136720   0.082819  -1.651   0.0988 .  
## Habitatpelagic-neritic  0.530651   0.108185   4.905 9.34e-07 ***
## ComImportmedium        -0.162080   0.075258  -2.154   0.0313 *  
## ComImportminor         -0.174222   0.113121  -1.540   0.1235    
## Spawnyes                0.056638   0.003802  14.896  < 2e-16 ***
## MonitArea_km2           0.027974   0.002851   9.811  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total7 <- glmmTMB(KUD95 ~ LengthStd + Habitat + ComImport + Spawn + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total7)
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ LengthStd + Habitat + ComImport + Spawn + MonitArea_km2 +  
##     (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9719.3   9809.0  -4848.7   9697.3    25601 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08333  0.2887  
##  Species     (Intercept) 0.02589  0.1609  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0746 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -0.177114   0.084316  -2.101   0.0357 *  
## LengthStd               0.214186   0.108057   1.982   0.0475 *  
## Habitatdemersal        -0.118887   0.083962  -1.416   0.1568    
## Habitatpelagic-neritic  0.513836   0.110339   4.657 3.21e-06 ***
## ComImportmedium        -0.161639   0.077914  -2.075   0.0380 *  
## ComImportminor         -0.113230   0.103585  -1.093   0.2743    
## Spawnyes                0.056620   0.003802  14.891  < 2e-16 ***
## MonitArea_km2           0.028508   0.002827  10.085  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Test residuals for the best model (goodness of fit)
testDispersion(Total7)  #plot with normality, dispersion and outliers
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 1.5858, p-value < 2.2e-16
## alternative hypothesis: two.sided
simulationOutput <- simulateResiduals(fittedModel = Total7, plot = F) #dispersion test
testDispersion(simulationOutput) #dispersion test

## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 1.5858, p-value < 2.2e-16
## alternative hypothesis: two.sided
plot(simulationOutput) #residual analysis

plotQQunif(simulationOutput) #Q-Q plot (normality checking)

plotResiduals(simulationOutput) #residual vs. predicted (homoscedasticity checking)

testOutliers(simulationOutput) #outliers checking

## 
##  DHARMa outlier test based on exact binomial test with approximate
##  expectations
## 
## data:  simulationOutput
## outliers at both margin(s) = 253, observations = 25612, p-value =
## 0.0008353
## alternative hypothesis: true probability of success is not equal to 0.007968127
## 95 percent confidence interval:
##  0.008703342 0.011166137
## sample estimates:
## frequency of outliers (expected: 0.00796812749003984 ) 
##                                            0.009878182
#Simulations from the model
getObservedResponse(Total7)  #response used to fit the model
##     [1]  1.079  0.839  3.494  0.819  1.162  0.764  0.893  1.385  1.284  1.772
##    [11]  1.433  1.812  2.506  1.990  0.762  0.762  0.879  0.990  1.074  1.063
##    [21]  1.078  1.020  1.103  0.909  1.131  0.865  0.864  0.874  0.898  0.873
##    [31]  1.736  1.943  1.911  1.812  1.923  1.906  1.931  1.969  1.818  1.800
##    [41]  1.884  1.790  1.642  1.577  1.780  1.762  1.762  1.803  1.782  1.871
##    [51]  1.819  0.929  1.496  1.629  1.738  1.714  1.779  1.851  1.842  1.362
##    [61]  1.697  1.587  1.923  1.977  1.733  2.198  2.102  2.254  2.059  1.993
##    [71]  1.755  1.862  1.833  1.751  1.797  1.726  1.699  1.685  1.774  1.814
##    [81]  1.914  1.724  1.840  1.799  0.856  1.668  1.138  1.003  0.902  0.960
##    [91]  0.912  0.840  0.899  0.903  0.913  0.916  1.003  0.931  0.836  0.840
##   [101]  0.835  0.795  0.827  0.841  0.829  0.842  0.820  0.818  0.813  1.044
##   [111]  1.117  1.126  1.121  1.096  1.077  1.160  1.012  1.020  1.716  0.983
##   [121]  0.868  0.858  1.061  1.100  0.883  0.885  0.850  1.673  1.012  1.060
##   [131]  0.874  0.862  0.998  1.788  1.553  1.132  1.659  1.383  0.919  0.835
##   [141]  0.924  0.956  0.955  0.839  1.104  1.002  1.093  1.125  1.072  1.019
##   [151]  1.011  1.138  1.078  1.072  1.099  1.083  0.962  1.107  1.078  1.113
##   [161]  1.116  1.278  0.937  1.136  1.134  1.130  1.103  1.118  1.119  1.102
##   [171]  1.121  0.933  0.855  1.259  1.454  1.069  0.912  0.786  0.997  1.621
##   [181]  1.606  1.702  1.469  1.001  1.512  1.652  1.264  1.221  1.552  1.881
##   [191]  0.928  1.161  0.967  1.043  1.138  0.951  1.273  0.818  0.893  0.816
##   [201]  0.918  1.083  1.064  0.918  0.889  1.055  0.961  1.123  0.951  1.128
##   [211]  1.111  1.037  1.106  1.119  1.101  0.949  1.427  1.653  1.950  1.734
##   [221]  1.041  0.937  1.963  2.062  0.924  0.928  1.187  2.061  1.679  1.728
##   [231]  1.763  1.623  1.945  2.114  1.274  1.029  1.531  1.430  1.611  0.938
##   [241]  0.958  0.919  0.856  0.910  0.851  0.805  0.803  0.838  0.920  0.799
##   [251]  0.781  0.782  0.936  0.878  1.037  1.020  1.141  1.177  1.373  1.298
##   [261]  1.230  1.159  1.210  1.188  1.225  1.349  1.124  1.059  0.979  1.912
##   [271]  1.478  1.047  0.982  1.227  1.516  1.508  1.334  1.612  1.280  1.173
##   [281]  1.508  1.366  0.966  1.781  1.802  1.390  1.100  0.911  0.923  0.996
##   [291]  0.818  1.075  0.946  0.980  0.959  1.040  1.117  1.061  1.005  1.113
##   [301]  1.134  1.094  1.076  1.200  1.083  1.057  1.119  1.030  1.037  0.992
##   [311]  1.013  1.051  1.055  1.023  0.938  0.959  1.049  1.120  1.107  1.138
##   [321]  1.166  1.222  1.304  0.794  0.879  0.912  1.146  1.060  1.103  1.457
##   [331]  1.487  1.597  1.793  1.560  1.959  1.751  1.593  1.651  1.028  1.648
##   [341]  1.137  1.148  1.386  1.306  1.435  1.373  1.538  1.389  1.529  1.386
##   [351]  1.502  1.803  1.854  1.887  1.829  2.110  2.254  1.646  1.921  2.071
##   [361]  2.030  1.990  1.820  1.599  2.043  1.736  1.996  2.074  2.948  2.175
##   [371]  1.843  2.186  1.510  1.825  1.847  1.512  1.349  1.623  0.984  0.933
##   [381]  1.253  1.123  1.405  0.948  1.535  0.792  0.790  0.775  0.807  0.771
##   [391]  0.782  0.776  0.769  0.771  0.791  1.307  1.012  0.868  1.220  1.172
##   [401]  0.864  0.845  0.950  1.023  0.969  0.807  0.879  1.169  1.054  0.916
##   [411]  1.289  1.037  1.305  1.052  1.356  1.685  1.732  1.323  1.282  1.253
##   [421]  1.806  1.748  1.215  1.174  1.096  1.403  1.235  1.584  0.897  0.864
##   [431]  0.912  0.829  0.848  0.824  0.803  0.798  0.807  0.970  0.940  0.817
##   [441]  0.824  0.822  0.797  0.801  0.808  0.796  0.870  0.884  0.900  0.901
##   [451]  0.816  0.904  0.937  0.940  1.833  1.672  0.977  0.979  1.051  1.554
##   [461]  0.793  0.831  0.914  0.871  0.847  0.939  0.953  0.899  0.860  0.884
##   [471]  0.908  0.915  0.867  0.836  1.064  0.850  0.863  0.810  0.980  0.870
##   [481]  1.015  0.923  1.050  1.082  1.042  1.041  1.112  1.040  1.022  1.012
##   [491]  0.961  1.032  0.898  0.785  0.829  1.058  1.049  0.905  0.788  0.789
##   [501]  1.784  1.293  1.504  1.516  0.980  1.488  1.827  1.453  1.136  1.815
##   [511]  1.667  1.423  1.564  0.882  1.024  1.042  1.078  0.792  0.812  1.198
##   [521]  1.227  1.228  1.578  1.262  1.117  1.325  1.080  1.150  1.578  1.570
##   [531]  1.509  1.192  1.720  1.520  1.606  1.407  1.252  1.264  1.392  1.353
##   [541]  0.821  1.172  1.328  1.354  1.917  1.520  1.332  1.505  1.495  1.195
##   [551]  1.459  1.257  1.298  1.074  1.277  1.266  1.222  1.120  1.471  1.298
##   [561]  1.300  1.172  1.374  1.229  1.178  1.183  1.311  1.176  1.209  1.148
##   [571]  1.181  1.648  0.820  0.800  0.801  0.788  0.797  0.800  0.808  0.809
##   [581]  0.786  0.793  0.794  0.794  0.799  0.808  0.804  0.906  0.983  0.939
##   [591]  0.895  0.893  0.819  0.795  0.834  0.791  0.795  0.785  0.800  0.893
##   [601]  0.782  0.773  0.800  0.971  0.772  0.770  0.776  0.782  0.796  0.784
##   [611]  0.861  0.767  0.823  0.771  0.783  0.800  0.802  0.924  0.803  0.799
##   [621]  0.821  0.791  0.810  0.805  0.793  1.081  1.066  1.094  1.102  1.066
##   [631]  1.132  1.074  1.092  1.121  1.156  1.111  1.114  1.083  1.143  1.102
##   [641]  1.159  1.124  1.169  1.293  1.341  1.360  1.340  1.164  1.033  1.166
##   [651]  1.230  1.358  1.294  1.302  1.567  1.338  1.318  1.270  1.243  1.183
##   [661]  1.476  1.362  1.350  1.227  1.201  1.229  1.226  1.149  1.142  1.378
##   [671]  0.842  1.247  1.120  1.142  1.088  1.089  0.825  0.794  0.827  0.794
##   [681]  0.834  0.890  1.146  0.807  0.790  0.798  0.810  0.801  0.915  0.794
##   [691]  0.793  0.826  0.823  0.799  0.849  1.262  1.639  0.784  1.391  1.830
##   [701]  1.669  1.432  1.491  0.802  0.783  0.879  0.829  0.802  0.789  1.356
##   [711]  1.359  1.358  1.275  1.311  1.316  1.341  1.710  1.299  1.196  1.537
##   [721]  1.658  1.686  1.190  1.413  1.537  1.576  1.309  1.142  1.431  1.566
##   [731]  1.650  1.246  0.983  1.213  1.288  1.420  1.486  1.516  1.163  1.411
##   [741]  1.341  1.353  1.226  1.527  1.327  1.322  1.541  1.491  1.442  1.525
##   [751]  1.271  1.068  1.128  1.629  1.797  1.642  1.285  1.459  1.786  1.494
##   [761]  1.735  1.736  1.705  1.679  1.314  1.364  1.604  1.620  1.654  1.694
##   [771]  1.752  1.825  1.719  1.522  1.848  1.710  1.457  1.441  1.630  1.880
##   [781]  1.862  1.816  0.880  1.535  1.834  1.413  1.054  1.721  0.982  1.405
##   [791]  0.824  0.988  1.009  1.137  1.448  1.327  1.469  1.413  1.212  1.428
##   [801]  1.462  1.422  1.252  1.300  1.519  1.468  1.070  0.948  0.926  1.033
##   [811]  1.079  0.955  0.840  0.843  0.875  0.796  1.380  0.939  0.766  0.764
##   [821]  1.330  1.037  1.384  1.940  2.013  1.703  1.880  2.046  2.011  1.764
##   [831]  1.500  1.890  1.996  1.501  1.546  2.650  1.915  1.464  1.350  1.783
##   [841]  1.422  1.911  1.807  1.402  1.183  1.336  1.625  1.705  1.199  1.687
##   [851]  2.112  1.626  1.959  1.773  1.752  2.016  1.840  1.673  1.341  1.148
##   [861]  1.394  1.083  0.857  0.858  2.583  1.746  1.788  1.391  1.767  1.625
##   [871]  1.467  1.423  0.877  1.167  0.761  0.762  0.762  0.762  0.762  1.015
##   [881]  1.123  1.147  1.307  1.277  1.235  1.200  1.286  1.199  1.448  1.339
##   [891]  1.381  1.672  1.310  1.180  1.548  1.501  1.462  1.208  1.507  1.414
##   [901]  1.419  1.495  1.280  1.463  0.762  0.762  0.762  0.761  0.761  0.765
##   [911]  0.763  0.761  0.763  0.766  0.761  0.765  0.762  0.761  0.761  0.761
##   [921]  0.762  0.761  0.761  0.761  0.792  0.761  2.018  1.115  0.847  0.965
##   [931]  1.270  1.032  1.309  1.631  1.245  1.355  1.385  1.173  1.299  0.802
##   [941]  0.769  1.195  0.936  0.764  0.771  0.762  0.764  0.762  0.761  0.761
##   [951]  0.762  0.762  0.761  0.761  0.766  0.929  0.929  1.006  1.019  0.950
##   [961]  1.620  1.587  1.661  1.777  1.652  1.582  0.867  1.094  1.653  2.291
##   [971]  2.184  2.684  2.598  1.782  0.776  1.723  0.869  0.821  1.863  1.561
##   [981]  1.742  1.348  1.415  1.623  1.732  1.820  1.722  1.850  2.007  1.863
##   [991]  1.962  1.820  1.859  1.988  2.412  0.761  1.761  1.655  1.063  1.189
##  [1001]  0.949  1.010  3.857  1.045  1.243  1.267  1.283  1.063  1.320  1.008
##  [1011]  1.348  1.335  1.352  1.281  1.295  1.263  1.209  1.264  1.137  1.028
##  [1021]  0.897  1.046  1.048  1.161  1.068  1.335  1.326  1.312  1.350  1.240
##  [1031]  1.696  2.186  2.458  0.761  2.594  1.289  1.153  1.233  1.358  1.258
##  [1041]  1.334  1.336  1.351  1.321  1.252  1.169  0.766  0.766  0.761  0.766
##  [1051]  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.762  4.133  3.133
##  [1061]  0.763  0.761  0.761  0.762  0.762  0.762  0.761  0.762  0.765  0.761
##  [1071]  0.766  0.762  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761
##  [1081]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [1091]  0.761  0.763  0.761  0.763  0.761  0.762  0.761  1.567  1.713  1.394
##  [1101]  1.585  0.762  1.573  1.312  1.335  0.898  1.456  0.806  0.826  0.839
##  [1111]  0.948  0.952  1.157  1.193  1.166  1.575  2.087  1.509  2.145  1.829
##  [1121]  2.255  1.498  2.222  1.760  2.122  2.226  2.227  2.248  2.205  2.066
##  [1131]  2.026  1.794  1.717  1.881  1.993  2.109  8.410  7.846  8.805  4.991
##  [1141]  3.834  6.338  8.343  6.595  7.410  7.560  6.803  7.530  5.109  5.559
##  [1151]  5.943  1.492  1.689  1.608  1.796  0.946  1.024  1.535  0.901  0.846
##  [1161]  1.620  1.297  1.415  1.588  1.749  1.275  1.054  1.532  1.882  1.158
##  [1171]  1.896  1.722  1.723  2.081  1.995  2.254  1.996  2.209  1.550  2.101
##  [1181]  1.674  1.667  1.710  2.215  1.516  2.192  2.050  1.932  2.043  2.179
##  [1191]  1.910  2.239  1.964  2.132  2.088  1.994  1.780  1.617  1.894  1.793
##  [1201]  1.687  1.499  2.307  1.906  1.819  1.806  1.459  1.925  1.010  1.700
##  [1211]  1.040  1.559  1.155  1.644  1.397  1.657  1.791  7.752  7.273  5.443
##  [1221]  2.245  2.754  1.496  2.823  3.655  7.074  7.297  2.410  1.601  1.642
##  [1231]  2.241  1.344  0.943  1.328  1.328  1.048  1.286  1.209  1.525  1.304
##  [1241]  1.098  1.445  1.052  1.681  1.442  1.669  1.765  1.081  1.165  1.429
##  [1251]  1.352  1.845  1.781  1.587  1.798  2.282  2.091  2.135  1.883  1.740
##  [1261]  1.844  1.747  1.659  1.516  1.819  1.826  1.748  1.761  1.779  1.619
##  [1271]  1.959  2.447  1.864  2.127  2.095  1.472  1.705  2.113  1.353  1.848
##  [1281]  1.058  1.857  1.120  1.042  1.304  1.038  1.756  1.016  1.517  1.041
##  [1291]  1.268  1.162  1.451  1.703  1.567  1.456  1.230  1.071  1.481  1.076
##  [1301]  1.601  1.389  2.114  2.345  2.211  2.835  3.964  4.869  2.149  2.740
##  [1311]  1.866  1.629  2.017  1.116  0.868  1.265  1.006  1.415  1.031  1.109
##  [1321]  1.083  1.302  1.334  0.914  0.978  1.148  1.365  1.361  1.522  1.421
##  [1331]  1.561  1.034  1.142  1.023  1.368  1.387  1.425  1.926  1.353  1.989
##  [1341]  1.929  1.787  1.932  1.717  1.104  1.477  1.329  1.820  1.283  1.489
##  [1351]  1.318  1.576  1.553  1.363  2.196  1.534  2.081  1.384  1.312  1.280
##  [1361]  1.091  0.828  1.322  1.192  1.372  1.388  1.413  1.291  1.356  1.260
##  [1371]  1.375  1.497  1.355  1.257  1.961  1.275  0.944  0.967  1.114  0.822
##  [1381]  0.862  1.068  1.708  1.787  1.947  1.828  1.726  1.726  1.640  1.593
##  [1391]  1.415  1.454  1.007  0.761  0.761  0.762  0.762  0.761  1.118  0.761
##  [1401]  0.762  0.861  1.252  0.761  0.761  0.762  0.761  0.764  0.762  0.761
##  [1411]  0.761  0.761  1.162  0.761  0.826  0.761  0.761  0.761  0.761  0.761
##  [1421]  0.761  0.761  0.761  0.761  0.762  0.762  0.762  0.761  0.761  0.877
##  [1431]  0.761  0.762  0.849  1.333  0.761  0.761  0.766  0.762  0.761  0.764
##  [1441]  0.762  0.761  0.764  1.285  0.761  0.761  1.325  1.009  0.761  0.761
##  [1451]  0.968  0.761  0.761  0.761  0.761  0.761  0.761  0.761  1.233  1.276
##  [1461]  1.411  0.764  0.761  0.762  0.761  0.761  1.088  0.761  0.761  0.761
##  [1471]  0.761  0.761  0.761  0.761  1.528  0.764  0.761  1.260  0.762  0.761
##  [1481]  0.761  1.044  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [1491]  0.761  0.761  1.375  1.527  2.818  1.516  1.310  1.263  0.761  1.516
##  [1501]  0.761  1.517  1.517  2.257  2.018  0.761  2.932  3.674  2.695  1.452
##  [1511]  3.017  1.376  1.517  2.218  1.516  1.526  0.761  1.524  0.762  0.762
##  [1521]  0.761  0.761  2.412  1.161  3.188  1.714  3.432  1.593  3.214  3.654
##  [1531]  0.761  1.736  1.173  0.761  0.761  0.761  0.761  1.097  0.761  0.761
##  [1541]  0.761  1.368  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [1551]  0.761  0.761  0.762  0.762  0.761  0.761  0.761  0.761  0.761  0.761
##  [1561]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.762
##  [1571]  0.761  0.762  0.761  0.761  0.761  0.762  1.513  1.194  1.468  0.762
##  [1581]  0.761  0.761  0.761  0.761  0.761  0.761  1.245  1.516  0.761  0.761
##  [1591]  0.766  0.762  0.761  0.901  0.761  0.761  0.761  0.762  0.761  1.526
##  [1601]  0.889  1.431  0.762  0.761  0.761  0.761  0.761  0.761  0.955  1.345
##  [1611]  1.516  0.761  0.761  0.766  0.762  0.761  0.895  0.761  0.761  0.761
##  [1621]  0.761  1.414  1.554  1.725  1.558  1.086  1.195  0.913  1.082  0.762
##  [1631]  0.761  0.762  0.761  0.764  0.761  0.761  0.984  1.414  0.926  0.945
##  [1641]  1.113  1.126  1.047  1.345  1.488  1.717  1.516  1.104  1.183  1.065
##  [1651]  1.007  0.890  0.904  0.764  0.762  0.761  0.761  1.023  0.761  0.761
##  [1661]  0.955  1.333  1.140  0.991  1.042  1.104  0.995  0.761  0.761  0.761
##  [1671]  0.761  1.524  1.809  1.516  1.148  0.766  0.762  0.761  1.091  0.766
##  [1681]  0.762  0.761  1.496  1.439  1.296  1.521  1.411  1.632  1.517  2.391
##  [1691]  1.276  1.257  1.387  0.761  0.762  2.068  2.110  0.763  3.779  2.213
##  [1701]  2.763  1.842  0.766  1.200  1.928  2.254  0.763  3.663  3.694  3.016
##  [1711]  2.175  0.766  0.761  0.761  0.761  0.761  0.764  0.761  0.761  0.761
##  [1721]  1.287  3.733  1.411  2.219  2.174  2.984  2.957  1.926  3.514  2.182
##  [1731]  2.023  3.849  1.464  1.485  3.615  3.768  2.181  1.453  2.203  1.745
##  [1741]  1.340  3.541  3.713  3.418  2.157  2.634  2.261  2.330  1.978  0.761
##  [1751]  0.761  0.764  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [1761]  0.761  0.761  0.761  0.761  0.761  0.764  0.761  0.761  0.761  0.761
##  [1771]  0.761  0.761  0.761  1.376  0.763  0.761  0.761  0.761  0.764  0.761
##  [1781]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [1791]  0.762  0.761  1.311  0.762  1.156  0.761  0.761  0.761  0.761  0.761
##  [1801]  0.761  0.761  0.761  0.761  0.761  1.313  2.478  1.670  0.761  2.204
##  [1811]  1.198  0.761  1.374  3.226  2.163  1.205  2.707  1.699  0.761  1.554
##  [1821]  1.420  1.319  1.009  1.496  0.764  2.753  2.795  1.534  1.398  1.223
##  [1831]  1.499  0.761  0.761  0.764  0.761  0.761  0.761  0.761  0.761  0.761
##  [1841]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.763  0.761  0.761
##  [1851]  0.761  0.764  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [1861]  0.761  0.761  0.761  0.761  0.970  1.171  1.482  0.766  0.762  0.761
##  [1871]  1.041  1.723  1.078  0.762  0.761  1.201  1.062  1.032  1.822  1.892
##  [1881]  1.728  1.765  1.019  0.903  0.839  1.651  1.841  1.628  1.715  1.411
##  [1891]  1.475  2.052  1.378  3.841  1.072  1.325  1.257  1.011  1.306  1.258
##  [1901]  0.761  0.761  0.883  1.036  0.761  0.761  0.836  1.083  1.742  1.213
##  [1911]  1.743  1.731  0.761  0.761  0.761  0.761  0.761  0.761  1.593  1.217
##  [1921]  0.761  0.761  1.482  1.697  1.357  1.497  1.525  1.694  1.636  1.483
##  [1931]  0.761  1.517  1.411  1.704  1.729  1.224  1.716  1.476  0.761  1.517
##  [1941]  1.342  1.725  1.694  1.668  1.742  1.466  2.171  2.124  0.761  1.501
##  [1951]  1.011  0.761  0.761  1.411  1.562  1.387  0.761  0.761  1.513  1.652
##  [1961]  1.475  1.103  0.761  0.761  1.453  1.639  1.572  0.927  0.761  1.517
##  [1971]  0.761  1.400  1.493  0.761  1.310  1.480  0.761  0.761  1.454  1.370
##  [1981]  0.764  0.762  0.761  0.761  1.388  1.442  0.927  1.297  1.494  0.761
##  [1991]  0.761  1.445  1.381  0.764  0.762  2.346  1.919  1.771  1.753  1.679
##  [2001]  1.620  1.666  1.674  1.723  1.672  1.742  1.753  1.691  1.645  1.695
##  [2011]  1.670  0.761  0.761  0.761  1.514  0.761  1.186  0.761  0.761  0.761
##  [2021]  0.761  0.761  0.761  1.535  0.761  0.761  0.761  0.761  1.517  0.761
##  [2031]  1.139  0.761  0.761  0.761  0.761  0.761  0.761  1.532  0.761  1.496
##  [2041]  0.761  0.761  0.761  0.761  0.761  0.760  0.761  0.761  0.761  0.761
##  [2051]  0.764  0.762  1.502  0.761  0.761  0.761  0.761  0.761  0.760  0.761
##  [2061]  0.761  0.761  0.761  0.764  0.762  1.152  1.737  1.647  2.019  1.461
##  [2071]  0.761  1.161  1.739  1.632  1.615  1.400  0.761  0.761  0.760  1.825
##  [2081]  0.832  0.760  0.760  0.760  0.761  0.761  0.760  0.761  0.760  1.803
##  [2091]  0.762  0.760  0.760  0.760  0.761  0.761  0.760  0.761  0.761  0.761
##  [2101]  0.761  0.761  0.761  0.761  0.761  1.326  0.972  0.894  0.893  1.219
##  [2111]  0.790  1.423  1.146  0.914  0.907  0.849  0.768  1.473  1.463  1.339
##  [2121]  1.477  1.499  1.475  1.524  1.508  1.523  1.447  1.353  1.165  1.469
##  [2131]  1.313  1.528  1.412  1.406  1.457  1.354  1.477  1.476  1.457  1.519
##  [2141]  1.495  1.523  1.461  1.386  1.213  1.457  1.339  1.526  1.877  1.490
##  [2151]  2.716  1.813  0.762  0.761  0.761  4.662  0.783  1.149  0.774  1.073
##  [2161]  0.812  1.844  2.007  0.968  0.814  1.411  0.874  1.950  1.488  0.839
##  [2171]  2.564  1.493  1.143  0.761  2.128  1.809  1.855  1.208  0.911  1.260
##  [2181]  1.691  0.822  1.260  1.256  2.799  0.985  0.762  0.761  4.898  0.870
##  [2191]  0.761  1.116  1.427  1.120  2.081  2.024  0.915  0.822  1.383  1.029
##  [2201]  1.862  1.338  0.793  2.253  1.220  1.032  0.761  2.233  1.458  1.218
##  [2211]  1.133  0.769  1.284  1.510  0.909  1.527  1.386  1.524  1.526  1.296
##  [2221]  1.526  1.339  1.457  1.513  0.761  1.363  1.487  1.413  1.535  1.521
##  [2231]  1.474  1.527  1.368  1.526  1.524  1.324  1.525  1.358  1.456  1.516
##  [2241]  0.761  1.369  1.470  1.453  1.531  1.527  1.474  0.763  5.602  0.906
##  [2251]  0.762  1.994  1.996  0.849  1.949  3.564  3.282  0.761  1.803  0.763
##  [2261]  0.860  3.306  2.570  2.866  3.495  1.590  3.759  1.740  0.897  3.517
##  [2271]  3.326  2.138  2.861  1.548  0.761  3.365  4.136  2.711  0.808  0.761
##  [2281]  0.761  0.762  1.819  4.482  2.947  2.276  1.314  3.196  1.902  1.644
##  [2291]  4.300  1.381  1.976  2.350  2.811  1.401  1.313  2.580  2.143  2.310
##  [2301]  2.124  3.354  1.304  2.123  1.903  2.123  2.701  1.344  0.761  2.731
##  [2311]  2.893  0.761  1.584  0.761  5.206  2.251  4.528  3.570  1.529  2.272
##  [2321]  2.751  2.619  1.178  4.502  2.775  4.483  5.689  2.514  7.286  1.729
##  [2331]  1.455  1.374  2.030  2.073  2.116  1.751  1.455  1.527  0.761  0.802
##  [2341]  3.374  1.154  2.435  1.619  3.440  0.762  0.762  4.270  4.847  3.389
##  [2351]  2.024  3.062  0.762  2.459  0.764  2.213  0.762  3.317  1.918  4.377
##  [2361]  1.743  6.218  1.832  3.935  3.502  4.627  2.368  4.496  4.090  4.301
##  [2371]  2.478  0.762  0.767  1.712  2.395  0.871  2.035  2.095  0.761  1.839
##  [2381]  1.440  1.066  1.679  0.761  0.761  0.763  2.505  1.345  0.761  0.761
##  [2391]  0.762  0.762  0.761  0.761  0.761  0.761  0.906  0.761  0.761  1.237
##  [2401]  0.763  1.703  0.817  0.761  0.761  0.761  1.300  0.761  0.761  0.761
##  [2411]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [2421]  0.761  0.762  0.761  0.763  0.761  0.761  0.761  0.761  2.816  1.241
##  [2431]  4.097  0.764  0.761  2.090  1.627  1.526  1.905  1.850  2.109  2.213
##  [2441]  3.372  3.291  3.445  3.413  2.982  1.836  1.906  1.931  2.053  1.857
##  [2451]  1.699  1.745  1.981  1.486  1.465  2.216  1.278  1.282  0.762  0.761
##  [2461]  2.483  1.034  1.163  1.038  6.556  1.254  1.512  1.343  1.315  1.077
##  [2471]  3.120  0.762  0.761  2.911  1.394  6.831  1.241  7.292  0.761  1.931
##  [2481]  1.201  1.817  3.749  2.091  2.894  1.424  1.379  0.761  4.314  3.429
##  [2491]  1.246  2.991  2.512  3.412  0.762  2.822  6.282  2.099  2.991  1.271
##  [2501]  1.982  1.894  1.640  1.848  1.592  1.698  1.824  3.602  1.951  1.676
##  [2511]  2.512  1.472  1.926  3.774  1.701  3.666  3.127  2.247  1.661  1.935
##  [2521]  2.295  1.880  0.764  1.493  3.747  4.277  3.346  4.987  4.750  5.731
##  [2531]  0.763  0.761  1.225  0.761  3.001  2.046  2.365  3.716  1.473  3.190
##  [2541]  4.881  1.336  1.393  3.413  1.250  1.578  1.574  1.403  1.519  1.254
##  [2551]  1.548  1.846  1.662  0.840  1.560  1.520  1.724  1.217  1.848  1.314
##  [2561]  1.191  1.357  1.440  1.611  1.502  1.527  1.501  1.386  1.464  1.541
##  [2571]  1.430  1.585  1.359  1.560  1.015  1.559  0.762  1.417  0.802  1.637
##  [2581]  0.762  1.271  0.762  1.293  0.762  1.561  1.569  0.762  1.543  0.762
##  [2591]  1.613  1.625  1.041  1.663  1.562  1.378  1.523  1.461  1.562  1.489
##  [2601]  1.407  1.481  1.451  1.424  1.547  1.693  1.920  1.825  1.957  1.993
##  [2611]  1.833  1.724  1.726  1.298  1.491  1.569  0.761  0.762  0.765  0.761
##  [2621]  0.761  0.761  0.763  0.761  0.763  0.761  0.761  0.761  0.761  0.761
##  [2631]  0.761  0.762  1.360  1.522  1.189  0.761  0.762  0.761  0.761  0.761
##  [2641]  0.761  4.983  9.142  2.883  3.143  2.938  3.088  2.740  2.557  1.244
##  [2651]  2.253  1.901  1.209  1.587  1.756  1.959  2.011  1.785  1.359  1.813
##  [2661]  1.960  1.343  0.761  0.761  0.761  0.766  0.761  0.762  0.761  0.761
##  [2671]  0.763  0.761  0.766  0.762  0.761  0.765  0.772  0.761  0.762  0.761
##  [2681]  0.856  1.201  0.890  0.787  0.766  0.761  0.778  0.764  1.333  0.761
##  [2691]  0.761  0.760  1.090  0.761  0.762  0.883  0.761  0.761  0.969  0.994
##  [2701]  0.873  1.077  0.855  0.761  0.762  0.762  0.761  0.761  0.761  0.761
##  [2711]  0.761  0.762  0.761  0.761  5.388  6.075  4.080  7.662  9.166  6.857
##  [2721]  6.955  6.223  5.543  8.678 10.692  6.300  9.127  5.232  6.299  4.754
##  [2731] 14.723  5.417 13.452  3.026 13.020  2.517  7.581  4.883  6.159  4.564
##  [2741]  2.584  7.030  3.266  1.220  4.325  3.196  3.482  2.349  2.465  2.893
##  [2751]  2.364  2.563  2.204  1.740  1.649  2.349  3.029  2.432  1.281  1.833
##  [2761]  1.610  1.877  1.676  1.678  1.719  2.304  1.937  1.629  3.234  2.231
##  [2771]  1.840  2.499  3.291  2.769  2.274  3.505  3.080  2.770  3.856  2.325
##  [2781]  2.550  1.968  1.409  5.251  1.598  4.289  6.615  2.360  3.621  3.010
##  [2791]  2.199  3.563  3.469  3.019  3.413  2.553  3.060  2.545  2.630  3.086
##  [2801]  3.400  4.225  3.862  3.509  2.972  2.876  3.812  1.743  3.649  7.300
##  [2811]  3.578  6.265  3.600  4.134  1.492  4.235  6.680  2.677  7.472  5.010
##  [2821]  4.652  4.589  2.969  3.581  3.344  2.251  2.843  3.097  3.553  5.515
##  [2831]  2.485  1.927  4.721  2.309  1.677  1.525  1.686  1.377  1.867  1.409
##  [2841]  1.350  1.302  1.741  1.737  2.428  2.190  2.251  3.297  2.576  2.354
##  [2851]  2.102  2.673  2.101  2.331  3.647  3.661  2.210  4.367  3.398  4.053
##  [2861]  3.764  3.244  3.430  2.425  1.981  3.465  2.070  1.910  3.141  2.196
##  [2871]  4.136  1.959  3.651  0.848  2.527  2.989  0.839  1.346  1.337  1.805
##  [2881]  1.907  2.204  2.283  1.883  1.468  1.362  1.361  1.453  1.482  1.435
##  [2891]  1.387  1.259  1.437  1.368  1.796  1.739 12.571  6.403  3.991  1.286
##  [2901]  1.369  1.425  1.470  1.436  1.354  1.397  1.352  1.308  1.301  1.286
##  [2911]  1.447  0.761  0.762  0.762  0.762  0.762  0.762  0.761  0.761  0.761
##  [2921]  0.761  0.761  0.761  0.761  0.761  1.003  1.076  0.800  1.081  1.504
##  [2931]  1.509  1.395  1.520  0.762  1.458  0.762  1.509  1.486  1.122  0.762
##  [2941]  1.433  1.397  1.408  2.756  2.322  1.474  1.519  1.634  1.030  0.977
##  [2951]  1.396  1.316  1.391  1.531  1.322  1.500  1.501  0.968  1.524  1.473
##  [2961]  1.190  1.407  1.550  1.526  1.497  1.776  1.685  1.683  1.353  1.492
##  [2971]  0.762  1.313  1.475  1.430  1.564  1.258  1.515  1.518  1.146  1.102
##  [2981]  1.454  1.807  0.992  0.761  1.106  1.972  1.087  1.082  0.761  0.761
##  [2991]  0.761  1.834  2.324  2.748  3.038  2.873  0.872  0.887  0.827  0.761
##  [3001]  0.761  0.761  0.796  0.771  0.771  0.771  0.761  0.761  0.761  0.761
##  [3011]  0.805  1.003  1.006  0.975  0.995  1.002  0.818  1.006  0.912  0.940
##  [3021]  0.958  0.988  0.973  0.766  0.964  0.774  0.784  0.893  0.767  0.770
##  [3031]  0.768  0.797  0.796  0.771  0.769  0.766  0.765  0.805  0.818  0.837
##  [3041]  0.818  0.790  0.846  0.770  0.769  0.762  0.761  0.761  0.761  1.021
##  [3051]  1.086  1.079  0.996  0.967  1.038  1.083  1.065  1.908  1.941  1.044
##  [3061]  1.897  1.227  2.058  0.765  0.764  1.068  1.031  1.057  1.064  1.043
##  [3071]  1.075  1.118  1.089  1.361  1.002  1.688  1.165  1.102  1.044  2.150
##  [3081]  1.071  1.061  1.846  1.956  1.932  1.566  1.741  1.013  1.036  0.941
##  [3091]  1.161  1.041  1.030  0.768  0.770  0.762  1.058  0.835  0.768  0.810
##  [3101]  0.821  0.891  0.928  0.875  0.885  0.907  1.302  1.232  1.231  0.819
##  [3111]  0.817  1.390  1.140  0.862  0.771  0.764  0.761  0.763  0.761  0.763
##  [3121]  0.762  0.762  0.761  0.762  0.762  0.764  0.762  0.762  0.762  0.762
##  [3131]  0.762  0.789  0.824  0.761  0.761  0.761  0.763  0.781  0.809  0.772
##  [3141]  0.859  0.771  0.795  0.960  0.978  0.991  0.985  1.002  1.082  1.657
##  [3151]  1.272  0.937  1.456  0.939  1.535  0.784  0.762  0.829  0.761  0.784
##  [3161]  0.761  0.762  0.762  0.768  0.762  0.761  0.762  0.774  0.765  0.762
##  [3171]  0.762  0.762  0.762  0.762  0.761  0.798  0.761  0.761  0.762  0.766
##  [3181]  0.808  0.761  0.864  0.879  0.761  0.763  0.777  0.777  0.767  0.766
##  [3191]  0.855  0.816  0.773  0.761  0.812  0.856  0.859  0.845  0.851  0.843
##  [3201]  0.849  0.862  0.868  0.832  0.848  0.853  0.844  0.834  0.837  0.865
##  [3211]  0.836  0.866  1.967  1.973  2.202  0.780  0.863  0.824  0.871  0.788
##  [3221]  0.835  0.769  0.762  0.762  0.762  0.762  0.761  0.976  0.857  0.761
##  [3231]  0.762  0.761  0.999  0.761  1.031  0.761  1.081  1.082  1.097  0.761
##  [3241]  0.819  0.822  0.827  0.852  0.806  0.840  0.976  0.762  0.867  0.834
##  [3251]  0.853  0.874  1.024  0.979  0.762  0.762  0.846  0.836  0.798  0.761
##  [3261]  0.964  1.116  1.025  2.199  1.493  2.869  0.977  2.814  2.126  2.711
##  [3271]  1.376  2.418  1.316  2.633  1.237  2.495  1.674  2.113  2.059  2.080
##  [3281]  1.955  2.544  2.072  1.294  0.761  1.905  2.230  2.271  0.761  1.336
##  [3291]  0.761  0.761  2.002  1.490  0.761  2.174  2.089  1.237  1.894  2.824
##  [3301]  2.801  2.088  1.891  2.229  1.899  1.349  2.322  2.326  0.761  1.838
##  [3311]  1.956  1.685  2.102  0.928  1.312  0.762  1.306  0.761  0.761  1.212
##  [3321]  0.915  0.761  0.761  0.876  0.761  0.761  0.761  0.761  0.761  1.219
##  [3331]  1.134  1.042  1.228  1.019  0.901  0.894  0.820  1.037  0.940  0.891
##  [3341]  0.854  1.050  1.091  0.904  1.069  1.101  1.088  1.049  0.953  1.884
##  [3351]  1.335  1.000  1.013  1.150  1.242  1.026  0.997  1.075  1.003  0.896
##  [3361]  0.992  0.912  0.921  0.870  0.761  0.763  0.761  0.800  0.886  0.761
##  [3371]  0.799  0.761  0.761  1.241  0.912  0.911  0.948  1.120  0.949  0.875
##  [3381]  0.905  1.023  0.924  1.001  0.984  1.047  1.006  0.988  1.041  1.050
##  [3391]  1.135  1.013  1.175  0.946  1.146  1.247  0.980  1.133  1.183  1.085
##  [3401]  1.173  1.099  1.410  1.056  1.260  0.858  0.950  0.790  0.798  1.049
##  [3411]  1.019  1.207  1.309  1.480  0.980  1.667  1.361  1.433  1.413  1.251
##  [3421]  0.998  1.097  1.262  1.069  0.998  0.887  0.991  0.997  0.995  0.945
##  [3431]  0.926  0.915  0.992  0.827  0.874  0.832  0.810  0.826  0.883  0.763
##  [3441]  0.864  0.761  1.450  1.023  0.761  0.948  1.025  0.914  1.137  0.806
##  [3451]  1.021  0.839  1.254  1.098  0.996  0.971  1.014  1.092  0.983  0.940
##  [3461]  0.998  0.762  1.190  1.027  0.988  0.910  1.019  0.971  0.803  0.933
##  [3471]  0.822  0.842  0.981  0.969  0.903  0.833  0.831  0.761  0.867  0.761
##  [3481]  1.006  0.995  0.977  0.852  1.000  0.761  1.281  1.558  1.350  1.475
##  [3491]  1.552  1.487  1.364  1.110  1.128  1.403  1.368  1.347  1.391  1.379
##  [3501]  1.385  1.096  1.496  1.168  1.624  1.417  1.569  0.998  1.152  1.176
##  [3511]  1.182  1.187  1.132  1.127  1.162  1.162  1.186  1.190  1.212  1.296
##  [3521]  0.965  0.761  0.761  0.761  0.761  0.761  0.761  0.979  0.761  0.761
##  [3531]  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3541]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3551]  0.762  1.191  0.762  0.762  0.762  1.167  0.762  0.762  0.762  0.762
##  [3561]  1.367  0.762  0.762  0.762  1.579  0.762  0.762  1.054  0.762  0.762
##  [3571]  0.921  0.762  1.021  0.762  0.762  0.762  0.762  1.387  0.762  0.762
##  [3581]  0.762  1.347  0.762  0.762  0.762  0.762  0.762  1.122  0.762  0.762
##  [3591]  0.762  1.710  0.762  0.762  1.389  0.762  0.762  0.762  0.823  0.762
##  [3601]  0.762  0.762  0.762  0.762  0.762  0.818  0.762  0.762  0.762  0.762
##  [3611]  0.762  0.762  0.762  0.762  1.217  0.762  0.762  0.762  0.823  0.762
##  [3621]  0.762  0.762  1.115  0.762  0.762  0.762  0.762  0.994  0.762  0.762
##  [3631]  0.813  0.762  0.762  0.762  0.904  0.762  0.762  0.762  0.762  0.762
##  [3641]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3651]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3661]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3671]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3681]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3691]  0.762  0.762  0.762  0.762  1.269  1.088  0.841  0.762  0.762  0.762
##  [3701]  1.048  0.762  0.870  0.762  0.762  0.762  1.201  0.762  1.065  0.762
##  [3711]  1.168  0.762  1.398  0.762  1.237  0.762  1.243  0.762  0.762  1.250
##  [3721]  0.762  0.762  1.049  0.762  0.762  0.762  1.015  0.762  0.762  0.898
##  [3731]  0.762  0.762  0.909  0.762  0.762  0.839  0.762  0.762  0.762  0.875
##  [3741]  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.762  0.762  0.762
##  [3751]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3761]  0.762  0.762  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3771]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3781]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3791]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3801]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3811]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3821]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3831]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  1.069  1.046
##  [3841]  1.024  0.996  1.251  0.937  0.849  0.846  0.823  0.850  0.897  0.786
##  [3851]  0.761  0.761  0.784  0.861  0.926  0.947  0.876  0.837  1.058  1.076
##  [3861]  1.055  0.763  0.815  0.793  0.813  0.774  0.778  0.787  0.904  0.867
##  [3871]  0.765  0.761  0.761  0.761  1.055  1.213  1.157  1.077  0.925  1.103
##  [3881]  1.200  1.528  1.117  1.146  1.160  1.244  1.241  1.039  1.174  1.189
##  [3891]  1.148  1.300  1.053  1.273  1.110  1.084  0.942  0.867  0.983  1.132
##  [3901]  0.939  1.214  0.913  0.785  0.761  0.761  0.847  0.761  0.762  0.804
##  [3911]  0.761  0.761  0.761  0.761  0.980  0.894  1.038  1.397  1.320  1.755
##  [3921]  1.596  1.341  1.113  0.908  0.775  0.869  1.031  1.008  0.979  0.938
##  [3931]  0.988  1.460  1.273  1.432  0.834  1.103  1.103  1.197  1.263  1.242
##  [3941]  1.249  1.286  1.328  1.201  1.215  1.127  1.257  1.286  1.291  1.292
##  [3951]  1.358  1.288  1.236  1.160  1.146  1.107  0.997  0.813  1.386  1.368
##  [3961]  1.265  1.267  1.201  1.137  1.140  1.282  0.858  0.965  1.057  1.173
##  [3971]  0.969  0.838  0.888  0.788  0.761  0.761  0.761  0.761  0.908  0.761
##  [3981]  0.761  1.139  0.917  1.380  0.761  0.761  1.127  1.033  0.761  0.777
##  [3991]  0.768  0.785  0.768  0.765  0.766  0.776  0.786  0.816  0.766  0.767
##  [4001]  0.768  0.801  0.798  0.762  0.762  0.889  0.915  0.770  0.792  0.875
##  [4011]  0.761  0.788  0.834  0.859  0.846  0.846  0.861  0.873  0.822  0.803
##  [4021]  0.814  0.812  0.900  0.857  0.866  0.859  0.968  0.994  0.924  0.871
##  [4031]  0.779  0.859  0.806  0.788  0.834  0.859  0.846  0.846  0.861  0.873
##  [4041]  0.822  0.803  0.814  0.812  0.900  0.837  0.786  0.984  0.886  0.886
##  [4051]  0.761  0.761  0.761  0.761  0.764  0.762  0.761  0.761  0.761  0.761
##  [4061]  0.761  0.761  0.774  0.764  1.244  1.205  1.125  0.781  0.762  0.796
##  [4071]  0.793  0.925  1.013  0.795  0.801  0.819  0.995  0.860  0.877  0.919
##  [4081]  1.029  0.794  1.037  1.117  1.013  0.803  0.845  0.827  0.838  0.772
##  [4091]  0.810  0.936  0.790  0.872  0.996  0.814  0.813  0.794  0.902  0.847
##  [4101]  0.787  0.786  0.852  0.838  0.848  0.833  0.802  0.771  0.838  0.838
##  [4111]  0.829  0.774  0.835  0.763  0.806  0.855  1.181  0.829  0.814  0.762
##  [4121]  0.934  0.761  1.029  0.981  0.935  0.863  0.932  0.924  0.988  0.984
##  [4131]  0.965  1.154  0.980  0.928  0.999  1.024  1.076  0.987  1.015  1.040
##  [4141]  1.028  1.003  0.991  0.904  1.013  1.066  1.088  1.050  1.056  1.031
##  [4151]  1.025  1.069  1.103  1.048  1.015  1.041  1.087  1.020  1.023  1.053
##  [4161]  1.034  1.010  1.040  1.071  1.031  1.024  1.053  1.081  1.047  1.021
##  [4171]  1.025  1.044  1.006  1.247  0.960  1.055  1.043  1.014  1.406  1.326
##  [4181]  1.210  0.980  0.766  0.767  0.863  1.646  1.436  1.583  1.686  1.592
##  [4191]  1.670  1.557  0.996  1.154  1.594  1.058  0.982  1.234  1.266  1.194
##  [4201]  1.177  1.183  1.046  1.023  0.988  0.990  1.046  0.957  0.989  1.010
##  [4211]  0.991  0.963  0.998  1.007  0.968  1.007  1.456  1.445  1.319  1.332
##  [4221]  1.187  0.925  1.058  1.052  1.080  1.171  1.051  1.169  1.153  0.927
##  [4231]  0.833  0.826  0.824  0.815  0.879  0.850  0.801  0.772  0.796  0.850
##  [4241]  0.831  0.851  0.796  0.823  0.899  0.852  0.812  0.808  0.808  0.802
##  [4251]  0.842  0.898  0.857  0.892  0.819  0.882  0.902  0.921  0.917  1.115
##  [4261]  1.029  1.018  1.007  1.001  0.980  1.049  1.560  1.107  1.106  0.989
##  [4271]  1.134  1.038  1.103  1.107  0.953  0.954  1.076  1.086  1.058  1.033
##  [4281]  0.924  1.014  1.080  1.069  1.043  1.072  1.040  1.067  1.093  1.096
##  [4291]  1.115  1.083  1.037  1.191  1.010  1.077  1.025  1.033  1.052  1.028
##  [4301]  1.066  0.974  0.894  0.887  0.962  0.927  0.899  0.990  0.998  1.158
##  [4311]  1.067  1.071  1.079  1.056  1.167  1.211  1.199  1.157  1.527  1.341
##  [4321]  1.106  1.128  1.106  1.162  1.045  1.103  1.148  1.063  1.019  1.015
##  [4331]  1.055  0.993  1.063  0.967  0.882  1.073  1.081  0.985  1.061  1.105
##  [4341]  0.931  0.885  0.927  0.796  0.818  0.761  0.839  1.098  0.809  0.882
##  [4351]  0.986  1.077  0.884  1.047  1.050  1.081  1.059  1.016  1.145  1.133
##  [4361]  1.114  1.087  0.978  1.170  1.238  1.091  1.208  1.016  1.130  1.018
##  [4371]  0.971  1.047  1.063  1.296  1.092  1.094  1.545  1.038  1.149  1.190
##  [4381]  1.045  1.108  0.969  1.070  1.100  1.322  1.512  0.892  1.067  1.078
##  [4391]  1.064  1.079  1.094  1.052  1.104  1.109  1.084  1.140  1.099  1.148
##  [4401]  1.044  0.987  1.097  1.069  1.077  1.096  1.047  1.019  1.080  1.058
##  [4411]  1.078  1.082  1.023  1.031  1.053  1.183  1.149  0.984  1.074  0.895
##  [4421]  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.836  0.848  0.772
##  [4431]  0.860  0.761  0.761  0.761  0.761  0.762  0.761  0.766  0.929  1.050
##  [4441]  0.973  1.012  1.053  0.982  1.059  1.048  1.060  1.127  1.090  1.065
##  [4451]  1.064  1.090  1.097  1.021  1.024  1.034  1.032  1.059  1.044  0.986
##  [4461]  1.149  1.024  1.010  1.068  0.988  0.936  0.988  0.792  0.761  0.869
##  [4471]  0.761  0.861  1.139  0.859  1.046  1.087  1.145  0.858  0.853  0.897
##  [4481]  1.020  0.957  0.916  0.981  1.155  0.796  0.989  0.966  1.036  0.843
##  [4491]  0.962  0.972  1.063  0.951  0.931  0.853  0.864  0.762  0.817  0.909
##  [4501]  0.864  0.761  0.761  0.761  0.889  0.888  0.961  0.969  0.900  0.793
##  [4511]  0.846  0.862  0.874  0.954  0.918  1.068  1.041  1.148  1.345  1.063
##  [4521]  0.848  1.036  1.027  0.875  0.801  0.860  0.935  0.868  0.844  0.915
##  [4531]  0.830  0.924  0.917  1.071  1.279  1.024  1.001  1.043  0.959  0.895
##  [4541]  0.914  1.015  0.875  0.838  0.845  0.903  1.054  0.921  0.888  0.828
##  [4551]  0.820  0.886  0.987  0.899  1.043  0.772  0.873  0.922  1.187  1.262
##  [4561]  1.119  1.111  1.219  1.180  1.238  1.118  0.964  1.424  1.199  1.609
##  [4571]  1.287  1.686  1.551  1.183  1.272  1.668  1.772  1.436  1.214  0.902
##  [4581]  1.156  1.285  1.212  1.153  1.173  1.165  1.238  1.248  1.243  1.012
##  [4591]  1.162  1.520  1.334  1.367  1.422  1.459  1.367  1.183  1.174  1.421
##  [4601]  1.459  1.431  1.332  1.287  1.358  1.246  1.203  1.209  1.156  1.328
##  [4611]  1.589  1.239  1.267  1.214  1.102  1.113  1.051  0.997  1.037  0.990
##  [4621]  0.992  0.910  1.244  1.001  1.131  1.097  1.320  1.221  1.062  0.847
##  [4631]  0.857  0.987  1.010  0.883  0.861  1.040  0.874  0.950  0.921  0.984
##  [4641]  0.918  0.893  0.878  1.020  1.040  0.970  0.915  0.895  0.941  0.995
##  [4651]  1.089  0.901  0.855  1.047  0.852  1.102  0.906  0.941  0.809  1.107
##  [4661]  0.942  0.911  0.891  1.142  1.168  1.016  1.168  0.938  0.891  1.024
##  [4671]  0.997  0.932  1.026  1.108  1.040  0.987  1.059  0.961  1.032  1.077
##  [4681]  1.435  1.133  1.027  1.300  0.976  1.050  0.856  1.023  0.985  1.040
##  [4691]  0.962  0.948  0.974  0.953  0.881  0.970  0.839  0.886  0.958  1.061
##  [4701]  0.852  1.106  0.962  0.963  1.044  1.167  1.026  1.008  1.012  0.981
##  [4711]  0.892  0.978  0.993  1.042  0.937  1.020  1.124  0.973  1.120  1.161
##  [4721]  1.096  1.220  1.240  1.151  1.155  1.106  1.112  1.147  1.193  1.049
##  [4731]  1.108  1.075  1.191  1.507  1.224  1.147  2.007  1.434  1.293  1.184
##  [4741]  1.056  1.288  1.262  1.347  1.283  1.238  1.436  1.322  1.411  1.314
##  [4751]  1.403  1.287  1.365  1.217  1.267  1.280  1.228  1.246  1.221  1.188
##  [4761]  1.112  1.226  1.164  1.147  1.261  1.202  1.274  1.024  1.315  1.323
##  [4771]  1.068  1.184  1.212  0.921  1.335  1.237  1.118  0.805  1.308  0.961
##  [4781]  0.926  1.184  1.042  1.354  1.167  1.566  1.378  1.115  0.801  1.003
##  [4791]  0.959  1.386  0.761  1.213  1.049  1.182  1.319  1.146  1.021  0.966
##  [4801]  0.930  1.153  1.134  1.029  1.175  1.002  1.174  0.940  0.998  0.941
##  [4811]  1.023  1.112  1.323  1.396  1.276  1.137  1.284  1.234  0.982  1.172
##  [4821]  0.993  1.538  1.308  1.135  1.296  1.140  1.310  1.285  1.288  1.261
##  [4831]  1.276  1.143  1.255  1.148  1.347  1.452  1.678  1.690  1.529  1.638
##  [4841]  1.278  1.379  1.415  1.137  1.877  1.131  1.432  1.386  1.427  1.363
##  [4851]  1.466  1.417  1.354  1.036  1.049  1.543  1.525  1.533  1.519  1.563
##  [4861]  1.546  1.562  1.467  1.570  0.997  1.253  1.199  1.115  1.159  1.209
##  [4871]  1.179  1.210  1.211  1.188  1.396  1.212  0.849  0.765  0.766  0.762
##  [4881]  0.768  0.779  0.779  0.785  0.774  0.776  0.794  0.819  0.858  0.840
##  [4891]  0.792  0.787  0.829  0.957  0.768  0.787  0.774  0.811  0.803  0.774
##  [4901]  0.784  0.767  0.762  0.762  0.913  0.824  0.816  0.786  0.844  0.812
##  [4911]  0.817  0.796  0.785  0.828  0.785  0.819  0.764  0.866  0.879  1.027
##  [4921]  0.973  1.048  0.989  1.017  1.053  0.986  1.017  1.004  1.050  1.035
##  [4931]  1.050  0.985  1.016  1.103  1.020  0.998  1.024  0.985  1.133  1.063
##  [4941]  0.868  1.014  0.945  0.951  1.014  1.069  0.999  1.050  1.071  1.154
##  [4951]  1.108  1.040  1.004  1.040  0.775  1.002  1.019  1.047  1.055  1.030
##  [4961]  1.068  1.055  1.013  1.045  0.942  1.042  1.063  0.970  1.056  1.042
##  [4971]  1.029  1.033  0.798  0.892  1.004  1.004  1.045  1.056  1.112  1.058
##  [4981]  1.090  1.098  1.124  1.117  0.827  0.934  1.089  1.096  1.107  1.084
##  [4991]  1.058  1.068  1.041  1.079  1.061  1.138  1.051  1.130  1.142  1.309
##  [5001]  0.969  1.321  1.844  1.522  0.827  1.031  1.068  1.282  0.810  0.796
##  [5011]  0.777  0.801  1.748  0.795  0.771  0.806  0.777  0.771  0.787  0.775
##  [5021]  0.795  0.801  0.775  0.777  0.775  0.773  0.791  0.799  1.064  0.761
##  [5031]  0.761  0.761  0.767  0.900  0.925  0.943  0.762  0.762  0.762  0.762
##  [5041]  0.762  0.762  0.762  0.766  0.766  0.763  0.761  0.761  0.761  0.763
##  [5051]  0.848  0.761  0.761  0.761  0.761  0.761  0.773  0.761  0.761  0.761
##  [5061]  0.761  0.761  0.761  0.827  0.761  0.761  0.797  0.793  0.812  0.807
##  [5071]  0.846  0.761  0.761  0.761  0.761  0.819  0.765  0.860  0.762  0.762
##  [5081]  0.765  0.762  0.766  0.765  0.776  0.770  0.851  0.796  1.031  1.309
##  [5091]  0.789  0.952  0.912  0.851  0.761  0.873  0.824  0.781  0.761  0.761
##  [5101]  0.761  0.761  0.761  0.761  0.761  0.849  0.811  0.791  0.788  0.825
##  [5111]  0.808  0.825  0.807  1.097  0.761  0.761  0.761  1.038  1.413  0.852
##  [5121]  0.892  0.837  0.762  0.829  0.762  0.763  0.783  0.772  0.784  0.804
##  [5131]  0.791  0.761  0.858  0.932  1.026  0.761  1.356  0.766  0.761  0.761
##  [5141]  0.761  0.761  0.761  0.761  0.761  0.761  0.865  0.828  0.801  0.824
##  [5151]  0.811  0.850  0.823  1.087  1.137  0.761  1.149  1.172  1.081  1.424
##  [5161]  0.964  0.784  0.770  0.774  0.771  0.770  0.819  0.765  0.773  0.773
##  [5171]  0.770  0.777  0.790  0.764  0.776  0.768  0.764  0.855  0.785  0.766
##  [5181]  0.765  0.767  0.802  0.792  0.780  0.801  0.788  0.772  0.774  0.762
##  [5191]  0.768  0.762  0.875  0.771  0.791  0.762  0.762  0.857  0.772  0.809
##  [5201]  0.781  0.799  0.781  0.762  0.769  0.778  0.776  0.771  0.766  0.763
##  [5211]  0.768  0.773  0.769  0.770  0.765  0.779  0.788  0.762  0.770  0.850
##  [5221]  0.962  0.763  0.762  0.762  0.762  0.904  0.765  0.767  0.763  0.761
##  [5231]  0.773  0.763  0.763  0.771  0.872  0.769  0.814  0.808  0.799  0.796
##  [5241]  0.902  0.810  0.794  0.786  0.803  0.788  0.789  0.811  0.816  0.813
##  [5251]  0.808  0.814  0.821  0.827  0.808  0.810  0.777  0.839  0.805  0.791
##  [5261]  0.797  0.790  0.769  0.762  0.762  0.762  0.829  0.801  0.772  0.806
##  [5271]  0.786  0.776  0.780  0.782  0.766  0.819  0.779  0.773  0.768  0.787
##  [5281]  0.837  1.310  0.868  0.786  0.784  0.762  0.765  0.762  0.762  0.794
##  [5291]  0.761  0.761  0.761  0.761  1.292  0.761  1.032  1.113  1.209  1.051
##  [5301]  0.761  0.761  0.865  0.761  0.761  0.761  0.761  0.761  0.761  0.791
##  [5311]  0.798  0.797  0.813  0.773  0.775  0.815  1.117  0.761  0.761  0.973
##  [5321]  1.116  0.979  0.837  0.761  0.765  0.793  0.773  0.769  0.783  0.782
##  [5331]  0.772  0.781  0.799  0.779  0.819  0.856  0.846  1.170  0.805  0.952
##  [5341]  0.873  1.444  0.820  0.785  0.805  0.797  0.807  0.822  0.817  0.838
##  [5351]  0.793  0.841  0.837  0.853  0.792  0.873  0.859  0.853  0.893  0.872
##  [5361]  0.875  0.847  0.823  0.840  0.877  0.902  2.365  2.365  0.819  0.793
##  [5371]  0.766  0.776  0.761  0.765  0.769  0.809  0.761  0.761  0.761  0.761
##  [5381]  0.762  0.766  1.064  0.978  1.035  0.981  1.072  0.956  1.010  0.979
##  [5391]  0.885  0.765  0.783  0.798  0.762  0.761  0.809  0.856  0.863  0.761
##  [5401]  0.910  0.761  0.761  0.761  0.761  0.761  0.761  0.761  1.824  0.864
##  [5411]  0.865  0.810  1.147  0.993  1.097  0.761  1.116  1.157  0.761  0.761
##  [5421]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [5431]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [5441]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  1.502  1.198
##  [5451]  0.763  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.766
##  [5461]  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.761  0.761
##  [5471]  0.761  0.761  0.761  0.821  0.836  0.789  0.761  0.823  0.761  0.761
##  [5481]  0.761  0.761  0.761  0.973  1.149  1.173  0.775  0.789  0.762  0.762
##  [5491]  0.837  0.762  1.154  1.690  0.848  0.828  0.828  0.761  0.761  0.761
##  [5501]  0.761  0.761  0.761  1.299  0.824  0.829  0.791  0.773  0.762  0.771
##  [5511]  0.762  0.762  0.762  0.762  0.761  0.761  0.802  0.773  1.337  0.761
##  [5521]  1.174  1.241  0.761  1.299  0.761  0.764  0.761  0.761  0.761  0.761
##  [5531]  0.761  0.761  0.919  0.975  0.871  0.875  1.079  0.936  1.098  0.851
##  [5541]  0.761  0.863  1.006  0.761  0.761  0.761  0.762  0.762  0.762  0.762
##  [5551]  0.762  0.762  0.762  0.762  0.762  0.761  2.038  0.764  0.764  0.762
##  [5561]  0.762  1.855  1.163  0.993  1.021  1.720  0.762  0.794  0.762  0.762
##  [5571]  0.762  0.762  0.803  0.803  1.244  0.761  1.166  0.761  0.761  0.762
##  [5581]  0.762  0.761  0.761  0.762  0.762  0.762  1.783  1.126  1.016  1.482
##  [5591]  1.638  1.998  1.679  1.699  1.722  1.628  1.096  1.700  1.762  2.446
##  [5601]  1.908  1.306  1.140  1.265  0.761  0.761  0.761  0.761  0.761  2.147
##  [5611]  1.410  1.222  1.176  1.856  0.762  0.762  0.762  0.762  0.762  0.762
##  [5621]  0.762  0.762  0.761  1.117  0.761  0.762  0.761  0.761  0.761  0.761
##  [5631]  0.761  1.260  0.920  1.009  1.144  1.121  1.276  1.084  1.128  1.155
##  [5641]  1.173  1.134  1.177  1.176  1.184  1.226  1.289  1.319  1.056  1.435
##  [5651]  1.130  1.011  1.000  1.065  3.222  1.734  1.109  1.031  0.770  1.812
##  [5661]  1.164  2.597  1.365  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [5671]  1.132  1.133  1.283  0.761  0.761  0.772  0.791  0.818  0.761  0.771
##  [5681]  0.762  0.815  0.822  0.769  0.819  0.778  0.795  0.844  0.900  0.762
##  [5691]  1.113  1.174  1.039  1.192  1.199  1.074  0.788  0.792  0.981  0.911
##  [5701]  1.143  1.300  1.211  0.829  0.885  0.761  0.787  0.800  0.893  1.244
##  [5711]  2.174  1.353  1.169  1.025  1.259  1.098  1.031  1.159  1.128  1.349
##  [5721]  1.140  1.066  1.193  1.180  1.276  1.295  1.149  1.144  1.058  1.203
##  [5731]  1.011  1.215  1.272  1.287  1.244  1.354  1.327  1.272  3.344  1.294
##  [5741]  1.400  1.296  1.374  0.761  1.203  0.815  0.792  0.762  1.010  1.095
##  [5751]  1.080  1.158  1.207  0.771  0.836  1.002  0.833  0.977  0.762  0.797
##  [5761]  0.985  0.813  0.763  0.762  0.796  0.881  0.914  0.832  1.117  0.764
##  [5771]  1.333  0.761  1.072  0.761  0.764  0.762  0.955  0.995  1.184  1.133
##  [5781]  0.999  1.279  1.100  0.916  0.770  0.885  0.996  1.058  0.957  1.035
##  [5791]  1.135  0.774  0.768  0.771  0.762  0.762  0.768  0.878  0.887  0.761
##  [5801]  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.762  0.833
##  [5811]  0.761  0.878  0.762  0.762  0.761  0.770  0.761  0.762  0.769  0.761
##  [5821]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [5831]  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.761  0.761
##  [5841]  0.835  0.761  0.761  0.761  0.762  1.123  1.256  1.408  0.761  0.765
##  [5851]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [5861]  0.761  0.761  0.764  0.761  0.995  1.062  0.762  0.895  0.761  0.761
##  [5871]  0.761  0.871  0.761  1.631  1.184  1.121  1.075  1.267  3.512  0.761
##  [5881]  0.761  0.761  0.761  0.761  0.761  0.804  0.761  0.761  0.814  0.813
##  [5891]  0.761  0.761  0.979  0.761  0.761  1.631  0.761  0.897  0.762  0.761
##  [5901]  2.297  1.059  0.762  1.158  0.761  0.761  1.074  1.253  1.264  1.245
##  [5911]  1.204  0.832  1.157  1.120  1.103  0.987  0.906  1.059  1.066  0.884
##  [5921]  0.999  1.054  0.783  0.786  0.782  1.187  1.268  0.786  0.766  1.231
##  [5931]  0.762  0.768  0.761  0.761  0.761  1.292  1.327  1.145  1.052  1.152
##  [5941]  1.124  1.108  1.051  1.598  1.568  1.727  1.552  1.684  0.761  0.761
##  [5951]  1.596  1.775  0.761  1.452  1.707  1.493  1.488  1.192  0.761  0.761
##  [5961]  0.762  0.762  0.761  0.761  0.762  0.762  1.220  1.941  1.927  0.761
##  [5971]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761
##  [5981]  0.761  0.761  0.761  0.761  2.037  1.246  1.844  0.761  0.762  0.762
##  [5991]  0.761  0.761  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [6001]  0.762  0.762  0.761  1.952  0.761  0.762  1.549  2.321  0.761  0.761
##  [6011]  0.761  0.761  0.762  0.762  0.761  0.761  0.761  0.762  0.762  0.762
##  [6021]  0.762  0.762  0.762  0.762  0.762  0.762  0.829  1.580  1.279  1.138
##  [6031]  1.264  1.212  2.022  1.286  0.761  0.761  0.761  0.761  0.761  0.761
##  [6041]  0.766  0.761  0.763  0.761  0.761  1.311  1.303  1.136  0.761  0.761
##  [6051]  0.761  0.761  0.761  1.769  1.898  2.127  2.362  1.746  0.761  0.761
##  [6061]  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  1.080
##  [6071]  0.761  1.912  0.761  1.374  0.761  0.762  0.998  1.139  1.195  1.115
##  [6081]  1.054  1.153  1.185  1.287  1.313  0.992  1.091  0.892  0.986  1.160
##  [6091]  1.321  1.109  1.398  1.187  0.787  0.813  1.029  1.066  0.920  1.114
##  [6101]  1.127  1.381  1.267  0.761  0.792  0.761  0.761  0.799  0.761  0.761
##  [6111]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [6121]  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761  1.514  1.309
##  [6131]  0.906  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.762  1.262
##  [6141]  0.939  1.333  0.987  0.815  0.778  0.804  1.386  1.188  1.038  0.761
##  [6151]  0.761  0.761  1.159  0.761  0.806  0.962  1.751  1.486  1.412  0.761
##  [6161]  1.323  0.761  0.761  0.761  0.761  0.761  0.834  0.940  1.208  1.284
##  [6171]  1.158  1.189  1.121  0.980  1.047  1.331  1.224  1.094  1.146  0.765
##  [6181]  1.138  0.972  1.326  1.038  1.355  0.995  0.763  0.807  0.844  0.913
##  [6191]  0.858  1.043  0.854  1.397  0.901  0.834  0.950  1.015  1.082  1.011
##  [6201]  1.060  1.046  1.085  1.069  1.113  1.121  1.094  1.081  1.157  1.139
##  [6211]  1.154  1.183  1.223  1.234  1.309  1.520  1.224  1.183  1.188  1.161
##  [6221]  0.995  1.217  1.133  0.761  1.848  0.761  0.761  0.761  0.762  0.762
##  [6231]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.761  1.272  0.761
##  [6241]  1.029  1.755  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [6251]  0.762  0.762  1.132  0.762  0.761  0.762  1.311  0.947  2.638  0.988
##  [6261]  1.070  1.137  1.105  1.219  1.216  1.253  1.216  1.185  1.213  1.204
##  [6271]  1.174  1.306  1.985  1.381  1.128  1.069  1.393  1.078  1.155  1.163
##  [6281]  1.211  1.290  1.075  1.181  1.015  0.764  1.167  0.886  0.887  0.762
##  [6291]  0.993  1.118  0.981  1.197  1.138  3.118  0.764  0.761  0.761  2.782
##  [6301]  1.025  1.056  0.796  0.776  0.802  0.764  0.932  0.799  0.762  0.838
##  [6311]  0.883  0.777  0.762  0.957  1.108  0.806  0.762  0.762  0.762  0.771
##  [6321]  0.761  0.762  0.762  0.762  0.862  0.761  1.288  0.764  0.761  0.761
##  [6331]  0.761  0.761  1.008  0.920  1.210  0.851  0.857  0.909  1.065  1.033
##  [6341]  0.955  0.882  0.988  1.050  0.820  0.964  0.785  1.280  0.764  1.042
##  [6351]  0.761  1.113  0.928  0.801  0.881  0.974  1.001  0.761  0.761  0.761
##  [6361]  0.762  0.762  0.981  1.206  1.098  1.144  0.955  0.947  0.841  0.820
##  [6371]  2.620  1.826  2.352  1.249  1.329  1.486  0.806  0.774  1.133  1.003
##  [6381]  1.054  1.309  1.126  1.153  1.066  1.104  1.246  1.070  1.134  0.761
##  [6391]  0.767  0.775  1.952  0.900  1.174  1.090  0.833  0.784  0.888  0.858
##  [6401]  0.862  1.002  1.044  1.111  1.149  1.074  1.033  1.024  1.174  1.329
##  [6411]  1.107  0.804  1.001  1.372  1.191  1.369  1.181  1.150  1.179  1.188
##  [6421]  1.082  1.179  1.027  0.999  1.214  1.217  1.348  2.056  0.769  1.511
##  [6431]  0.761  0.762  0.761  0.761  0.761  0.761  1.618  0.961  0.906  1.163
##  [6441]  1.148  1.183  1.302  1.029  1.174  1.209  1.316  1.224  1.196  1.118
##  [6451]  1.208  1.281  0.911  0.968  1.353  0.762  0.899  1.001  1.003  1.044
##  [6461]  0.935  0.899  0.944  0.802  2.113  1.383  1.134  1.131  1.217  1.227
##  [6471]  1.155  1.212  1.011  1.178  1.196  0.761  1.213  1.215  1.237  1.130
##  [6481]  0.824  0.963  1.156  1.012  0.800  0.811  1.129  1.245  0.877  0.958
##  [6491]  1.008  1.130  3.690  0.933  0.765  1.435  1.226  2.928  1.320  0.813
##  [6501]  0.852  0.899  0.761  0.762  0.814  0.805  0.827  0.798  0.955  0.837
##  [6511]  1.860  1.398  0.937  0.874  1.348  0.982  0.761  0.769  0.786  0.761
##  [6521]  0.761  0.761  0.761  0.762  0.760  0.901  0.762  0.761  0.761  0.761
##  [6531]  0.761  0.761  0.761  0.761  0.853  0.943  0.947  1.190  1.863  3.208
##  [6541]  1.336  0.920  1.005  3.019  0.761  0.788  0.766  0.943  1.014  0.761
##  [6551]  0.762  0.762  0.760  1.100  0.761  1.164  1.103  1.140  1.080  1.036
##  [6561]  0.818  0.762  0.780  0.809  0.789  0.818  0.965  0.762  2.057  1.061
##  [6571]  0.892  1.023  1.267  0.992  0.957  0.966  0.892  0.966  1.178  1.036
##  [6581]  1.273  1.156  1.029  1.007  1.225  1.943  1.217  0.762  0.762  0.762
##  [6591]  0.762  0.762  0.762  0.761  0.762  0.761  0.762  0.762  0.762  0.762
##  [6601]  0.762  1.221  0.761  1.404  0.761  0.762  0.762  0.762  0.762  0.851
##  [6611]  1.024  0.762  1.211  0.782  0.762  1.767  1.202  1.076  1.201  1.061
##  [6621]  0.761  1.113  3.092  1.218  1.142  1.483  0.761  0.761  0.761  0.761
##  [6631]  0.761  0.761  0.761  0.943  0.761  1.759  0.866  1.112  0.761  0.816
##  [6641]  0.761  0.761  0.761  0.761  0.761  0.762  0.762  0.762  0.762  0.762
##  [6651]  0.762  0.762  0.762  0.764  0.762  1.478  0.762  0.761  0.762  0.762
##  [6661]  0.762  0.762  0.762  0.762  0.761  0.764  2.367  0.761  0.873  0.762
##  [6671]  1.313  1.866  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [6681]  0.764  0.762  0.760  0.762  0.761  0.764  0.787  0.796  0.761  0.864
##  [6691]  1.248  1.009  1.163  0.831  1.703  1.238  0.761  0.762  0.962  1.368
##  [6701]  1.177  1.950  1.052  0.762  0.924  0.932  0.846  0.762  0.897  0.762
##  [6711]  0.905  0.761  0.761  0.762  0.762  0.995  0.761  0.761  0.761  0.761
##  [6721]  0.761  0.902  0.761  0.761  0.761  0.777  0.761  1.447  1.470  0.761
##  [6731]  0.761  0.761  1.857  0.762  1.585  0.775  0.761  0.761  0.765  0.762
##  [6741]  0.762  0.761  0.763  0.765  0.762  0.762  0.762  0.762  0.765  0.765
##  [6751]  0.762  0.762  0.762  0.765  0.765  0.762  0.762  0.761  0.766  0.765
##  [6761]  0.762  0.762  0.761  0.765  0.765  0.762  0.762  0.762  0.765  0.761
##  [6771]  0.762  0.762  0.761  0.765  0.765  0.762  0.762  0.762  0.761  0.765
##  [6781]  0.765  0.762  0.762  0.762  0.766  0.785  0.825  0.762  0.762  0.761
##  [6791]  0.765  0.791  0.762  0.762  0.761  0.766  0.870  0.762  0.762  0.761
##  [6801]  0.765  0.765  0.762  0.762  0.761  0.765  0.765  0.762  0.765  0.808
##  [6811]  0.765  0.765  0.762  0.935  0.761  0.765  0.761  0.762  0.762  0.761
##  [6821]  0.765  0.765  0.762  0.762  0.761  0.765  0.765  0.762  0.761  0.761
##  [6831]  0.765  0.765  0.762  0.762  0.761  0.765  0.762  0.762  0.761  0.765
##  [6841]  0.762  0.761  0.761  0.765  0.762  0.762  0.761  0.765  0.762  0.761
##  [6851]  0.761  0.765  0.762  0.762  0.761  0.765  0.762  0.762  0.761  0.765
##  [6861]  0.762  0.762  0.761  0.765  0.762  0.761  0.766  0.765  0.762  0.762
##  [6871]  0.765  0.761  0.765  0.762  0.762  0.765  0.762  0.765  0.765  0.762
##  [6881]  0.765  0.765  0.762  0.765  0.765  0.762  0.761  0.765  0.761  0.765
##  [6891]  0.762  0.762  0.765  0.761  0.787  0.762  0.786  0.765  0.761  0.765
##  [6901]  0.762  0.762  0.765  0.761  0.765  0.762  0.761  0.765  0.761  0.765
##  [6911]  0.762  0.762  0.762  0.761  0.761  0.765  0.762  0.762  0.762  0.761
##  [6921]  0.761  0.765  0.762  0.762  0.762  0.761  0.761  0.765  0.762  0.762
##  [6931]  0.765  0.761  0.761  0.765  0.762  0.762  0.761  0.761  0.761  0.765
##  [6941]  0.762  0.762  0.762  0.761  0.761  0.765  0.762  0.762  0.762  0.761
##  [6951]  0.761  0.761  0.762  0.762  0.762  0.761  0.761  0.765  0.762  0.762
##  [6961]  0.762  0.761  0.761  0.765  0.762  0.762  0.762  0.761  0.762  0.765
##  [6971]  0.762  0.762  0.762  0.761  0.761  0.763  0.762  0.762  0.761  0.761
##  [6981]  0.761  0.765  0.762  0.762  0.762  0.761  0.761  0.765  0.762  0.762
##  [6991]  0.762  0.761  0.762  0.761  0.762  0.762  0.765  0.761  0.767  0.765
##  [7001]  0.762  0.762  0.763  0.766  0.765  0.762  0.762  0.764  0.765  0.765
##  [7011]  0.762  0.762  0.764  0.765  0.765  0.762  0.762  0.761  0.767  0.765
##  [7021]  0.762  0.762  0.763  0.765  0.765  0.762  0.762  0.764  0.767  0.761
##  [7031]  0.762  0.762  0.762  0.765  0.765  0.762  0.762  0.761  0.766  0.764
##  [7041]  0.762  0.762  0.765  0.763  0.763  0.762  0.762  0.762  0.765  0.762
##  [7051]  0.762  0.762  0.763  0.766  0.762  0.762  0.762  0.761  0.765  0.765
##  [7061]  0.762  0.762  0.761  0.765  0.765  0.762  0.761  0.761  0.766  0.765
##  [7071]  0.762  0.761  0.761  0.766  0.762  0.762  0.762  0.761  0.765  0.765
##  [7081]  0.762  0.762  0.761  0.765  0.765  0.762  0.761  0.761  0.765  0.765
##  [7091]  0.762  0.762  0.761  0.765  0.762  0.762  0.761  0.765  0.762  0.761
##  [7101]  0.761  0.765  0.762  0.762  0.761  0.765  0.762  0.761  0.761  0.945
##  [7111]  0.762  0.762  0.761  0.892  1.161  0.762  0.762  0.880  1.158  1.270
##  [7121]  1.169  0.833  0.821  1.011  1.321  0.827  0.854  1.288  0.995  0.956
##  [7131]  1.261  0.946  1.296  0.899  1.205  0.955  1.175  0.832  1.298  0.839
##  [7141]  1.316  0.873  1.197  0.853  0.767  1.146  0.825  0.762  0.765  1.226
##  [7151]  0.765  0.762  0.815  0.768  0.767  0.765  0.762  0.765  0.762  0.762
##  [7161]  0.761  0.765  0.761  0.765  0.762  0.765  0.761  0.765  0.761  0.765
##  [7171]  0.762  0.765  0.762  0.762  0.761  0.761  0.765  0.762  0.762  0.761
##  [7181]  0.761  0.765  0.762  0.762  0.761  0.762  0.765  0.762  0.761  0.761
##  [7191]  0.762  0.765  0.762  0.761  0.761  0.762  0.765  0.762  0.762  0.761
##  [7201]  0.763  0.765  0.762  0.762  0.761  0.762  0.761  0.762  0.762  0.761
##  [7211]  0.763  0.765  0.762  0.762  0.761  0.763  0.765  0.762  0.762  0.763
##  [7221]  0.764  0.765  0.762  0.762  0.765  0.762  0.764  0.762  0.761  0.762
##  [7231]  0.765  0.765  0.762  0.762  0.763  0.764  0.765  0.762  0.762  0.762
##  [7241]  0.804  0.761  0.762  0.768  0.761  0.762  0.765  0.762  0.762  0.762
##  [7251]  0.761  0.764  0.765  0.762  0.762  0.762  0.762  0.765  0.765  0.762
##  [7261]  0.762  0.762  0.761  0.765  0.765  0.762  0.762  0.762  0.761  0.765
##  [7271]  0.765  0.762  0.762  0.762  0.761  0.765  0.765  0.762  0.762  0.762
##  [7281]  0.761  0.765  0.761  0.762  0.762  0.762  0.761  0.765  0.765  0.762
##  [7291]  0.762  0.762  0.761  0.765  0.765  0.762  0.762  0.762  0.766  0.762
##  [7301]  0.762  0.762  0.762  0.762  0.761  0.765  0.762  0.762  0.762  0.762
##  [7311]  0.762  0.765  0.762  0.762  0.762  0.762  0.761  0.765  0.765  0.762
##  [7321]  0.762  0.805  0.761  0.765  0.765  0.762  0.762  0.761  0.761  0.765
##  [7331]  0.765  0.762  0.762  0.784  0.761  0.765  0.761  0.762  0.762  0.762
##  [7341]  0.761  0.765  0.765  0.762  0.762  0.762  0.761  0.765  0.765  0.762
##  [7351]  0.762  0.761  0.783  0.765  0.765  0.762  0.762  0.762  0.761  0.765
##  [7361]  0.762  0.762  0.762  0.761  0.765  0.762  0.762  0.761  0.761  0.765
##  [7371]  0.762  0.762  0.780  0.761  0.765  0.762  0.761  0.761  0.765  0.762
##  [7381]  0.762  0.761  0.765  0.762  0.762  0.761  0.765  0.762  0.762  0.761
##  [7391]  0.765  0.762  0.761  0.781  0.765  0.762  0.762  0.765  0.761  0.765
##  [7401]  0.762  0.762  0.765  0.762  0.765  0.761  0.765  0.762  0.765  0.765
##  [7411]  0.762  0.765  0.765  0.762  0.761  0.765  0.761  0.765  0.762  0.762
##  [7421]  0.765  0.762  0.762  0.762  0.761  0.765  0.761  0.765  0.762  0.762
##  [7431]  0.765  0.761  0.765  0.762  0.761  0.765  0.761  0.765  0.762  0.762
##  [7441]  0.762  0.761  0.761  0.765  0.762  0.762  0.762  0.761  0.762  0.765
##  [7451]  0.762  0.762  0.762  0.761  0.761  0.765  0.762  0.762  0.761  0.761
##  [7461]  0.761  0.765  0.762  0.762  0.761  0.761  0.761  0.765  0.762  0.762
##  [7471]  0.762  0.761  0.761  0.765  0.762  0.762  0.762  0.761  0.762  0.761
##  [7481]  0.762  0.762  0.762  0.761  0.761  0.765  0.762  0.762  0.762  0.761
##  [7491]  0.761  0.765  0.762  0.762  0.762  0.761  0.761  0.765  0.762  0.762
##  [7501]  0.762  0.769  0.762  0.763  0.762  0.762  0.762  0.761  0.762  0.765
##  [7511]  0.762  0.762  0.762  0.761  0.762  0.765  0.762  0.762  0.762  0.761
##  [7521]  0.773  0.761  0.762  0.762  0.764  0.762  0.943  0.763  0.763  0.795
##  [7531]  0.762  0.970  0.763  0.788  0.950  0.907  0.913  0.763  0.763  0.827
##  [7541]  0.764  0.773  0.763  0.763  0.763  0.826  0.765  0.763  0.763  1.340
##  [7551]  0.855  0.797  0.763  0.952  0.872  0.815  0.862  0.763  0.763  0.832
##  [7561]  0.874  0.841  0.763  0.763  0.787  0.906  0.810  0.763  0.763  0.848
##  [7571]  0.762  0.849  0.763  0.763  0.981  0.824  0.762  0.763  0.763  0.763
##  [7581]  0.843  0.764  0.763  0.865  0.763  0.803  0.764  0.763  0.763  0.845
##  [7591]  0.772  0.764  0.763  0.763  0.761  0.764  0.785  0.763  0.763  0.991
##  [7601]  0.845  0.801  0.763  0.763  0.792  0.901  0.764  0.763  0.763  0.802
##  [7611]  0.762  0.819  0.764  0.763  0.763  0.761  0.762  0.842  0.770  0.763
##  [7621]  0.966  0.811  0.826  0.761  0.763  0.763  0.763  0.765  0.763  0.763
##  [7631]  0.762  0.912  0.763  0.763  0.762  0.912  0.763  0.762  1.035  0.763
##  [7641]  0.885  0.762  1.036  0.761  0.763  0.762  1.081  0.762  1.038  1.457
##  [7651]  0.857  0.762  0.973  0.762  1.036  0.946  0.766  0.762  1.072  1.054
##  [7661]  1.059  0.909  0.763  0.762  1.059  0.761  1.018  0.890  0.762  1.058
##  [7671]  0.761  1.044  0.815  0.924  1.006  0.761  1.034  0.828  0.870  1.059
##  [7681]  0.761  0.970  0.886  0.987  1.019  0.761  1.002  0.917  0.851  0.910
##  [7691]  0.761  0.881  0.869  1.047  0.764  0.762  0.920  0.808  0.806  0.764
##  [7701]  0.762  0.762  0.788  0.763  0.764  0.853  0.762  0.765  0.764  0.762
##  [7711]  0.825  0.760  0.763  0.764  0.762  0.832  0.763  0.766  0.995  0.762
##  [7721]  0.764  0.763  0.763  1.048  0.762  0.768  0.763  0.763  0.932  0.762
##  [7731]  0.827  0.763  0.763  0.929  0.762  0.791  0.763  0.763  0.930  0.762
##  [7741]  0.799  0.763  0.763  0.763  0.762  0.790  0.763  0.763  0.765  0.762
##  [7751]  0.761  0.763  0.763  0.763  0.762  0.764  0.763  0.763  0.788  0.761
##  [7761]  0.764  0.763  0.763  0.896  0.762  0.764  0.763  0.763  0.858  0.762
##  [7771]  0.848  0.763  0.873  0.902  0.762  1.036  0.763  0.783  0.843  0.762
##  [7781]  0.909  0.763  0.763  0.872  0.762  0.906  0.763  0.763  0.762  0.763
##  [7791]  0.762  1.034  1.049  1.046  0.858  0.762  1.044  1.049  1.023  0.876
##  [7801]  0.762  1.072  1.053  1.042  1.038  1.032  0.896  1.044  0.983  0.976
##  [7811]  1.084  0.762  0.951  1.052  0.987  1.015  1.046  0.993  1.061  0.987
##  [7821]  1.031  1.077  0.762  1.013  1.037  1.052  1.018  1.056  0.855  1.059
##  [7831]  1.036  1.024  1.073  0.762  0.764  1.039  1.027  1.002  1.044  0.997
##  [7841]  0.804  1.023  0.989  0.993  1.087  0.762  0.986  1.046  1.002  1.057
##  [7851]  1.087  0.766  1.040  1.051  1.004  0.925  0.764  0.898  1.042  1.046
##  [7861]  0.874  0.775  1.022  1.009  1.046  0.879  0.884  1.005  1.002  1.042
##  [7871]  0.876  0.764  0.969  0.954  0.956  0.864  0.816  0.998  0.893  0.953
##  [7881]  0.833  0.818  1.061  0.984  1.029  0.908  0.767  1.042  1.054  1.050
##  [7891]  0.911  0.765  0.761  1.021  1.020  0.907  0.762  0.782  0.761  1.042
##  [7901]  1.050  0.853  0.768  0.761  1.039  1.090  0.839  0.764  0.761  1.011
##  [7911]  0.762  0.989  0.762  0.799  0.761  1.006  0.762  0.910  0.762  0.943
##  [7921]  5.591  1.031  0.762  1.005  0.762  0.944  1.637  1.004  0.762  0.942
##  [7931]  0.762  0.971  0.976  1.015  0.762  1.080  0.761  0.997  0.976  0.975
##  [7941]  0.762  1.018  0.761  0.985  1.007  0.762  1.015  0.761  1.014  1.014
##  [7951]  0.976  0.913  0.761  0.999  1.003  1.036  1.028  0.761  1.049  1.030
##  [7961]  0.998  0.945  0.761  1.025  1.023  1.025  1.057  0.761  1.040  1.050
##  [7971]  0.993  0.935  0.762  0.806  1.031  1.013  0.908  0.920  1.040  1.008
##  [7981]  0.982  0.950  0.762  0.975  0.923  0.762  0.970  1.050  1.014  0.770
##  [7991]  0.960  0.762  0.906  1.053  1.006  1.059  0.762  1.009  1.052  1.006
##  [8001]  1.085  0.762  1.001  1.039  0.984  1.080  0.762  0.762  0.917  1.046
##  [8011]  1.032  1.073  0.762  0.762  1.051  1.044  1.001  1.061  0.762  0.762
##  [8021]  1.043  1.050  1.005  1.047  0.762  1.061  1.032  0.910  1.079  0.762
##  [8031]  0.762  0.822  1.009  1.009  1.049  0.762  0.762  0.938  1.055  0.995
##  [8041]  1.048  0.761  0.987  1.030  1.046  0.969  0.762  0.858  1.028  1.045
##  [8051]  1.044  0.762  0.762  0.851  1.040  1.057  1.082  0.762  0.762  1.023
##  [8061]  1.029  1.021  1.051  0.762  1.039  1.033  1.021  1.016  0.762  1.018
##  [8071]  1.032  1.007  0.762  0.818  0.818  0.792  0.762  0.832  0.846  0.762
##  [8081]  0.811  0.824  0.762  0.761  0.817  0.762  0.817  0.816  0.762  0.828
##  [8091]  0.818  0.762  0.846  0.802  0.762  0.834  0.817  0.762  0.828  0.827
##  [8101]  0.828  0.819  0.762  0.807  0.823  0.762  0.813  0.817  0.762  0.838
##  [8111]  0.831  0.761  0.849  0.813  0.761  0.847  0.821  0.762  0.835  0.830
##  [8121]  0.762  0.822  0.818  0.761  0.830  0.831  0.762  0.828  0.816  0.762
##  [8131]  0.822  0.823  0.761  0.830  0.815  0.762  0.800  0.810  0.761  0.830
##  [8141]  0.818  0.762  0.817  0.806  0.762  0.809  0.812  0.762  0.807  0.805
##  [8151]  0.761  0.826  0.828  0.762  0.761  0.802  0.761  0.761  0.821  0.762
##  [8161]  0.761  0.818  0.864  0.761  0.845  0.761  0.828  0.761  0.781  0.815
##  [8171]  0.856  0.803  0.785  0.801  0.824  0.800  0.807  0.811  0.804  0.798
##  [8181]  0.812  0.805  0.796  0.813  0.806  0.830  0.812  0.821  0.803  0.805
##  [8191]  0.767  0.835  0.834  0.810  0.847  0.799  0.803  0.797  0.804  0.802
##  [8201]  0.782  0.805  0.815  0.817  0.848  0.817  0.809  0.761  0.762  0.761
##  [8211]  0.762  0.762  0.762  0.762  0.762  0.761  0.762  0.761  0.762  0.761
##  [8221]  0.762  0.761  0.762  0.761  0.762  0.766  0.762  0.761  0.762  0.763
##  [8231]  0.762  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.762  0.761
##  [8241]  0.762  0.761  0.761  0.761  0.762  0.761  0.762  0.761  0.761  0.761
##  [8251]  0.762  0.763  0.761  0.761  0.762  0.761  0.762  0.761  0.762  0.761
##  [8261]  0.761  0.798  0.762  0.761  0.761  0.927  0.762  0.765  0.766  0.765
##  [8271]  0.761  0.767  0.762  0.765  0.761  0.766  0.762  0.765  0.761  0.765
##  [8281]  0.762  0.761  0.762  0.761  0.762  0.761  0.761  0.761  0.761  0.761
##  [8291]  0.762  0.761  0.762  0.761  0.762  0.761  0.762  0.761  0.762  0.762
##  [8301]  0.762  0.761  0.761  0.765  0.762  0.761  0.762  0.761  0.761  0.784
##  [8311]  0.783  0.762  0.762  0.791  0.815  0.828  0.762  0.762  0.776  0.795
##  [8321]  0.814  0.762  0.762  0.830  0.822  0.773  0.762  0.762  0.761  0.845
##  [8331]  0.798  0.762  0.762  0.776  0.846  0.773  0.762  0.762  0.764  0.834
##  [8341]  0.761  0.762  0.762  0.804  0.815  0.852  0.762  0.762  0.772  0.824
##  [8351]  0.858  0.762  0.762  0.812  0.822  0.839  0.762  0.762  0.815  0.835
##  [8361]  0.847  0.762  0.762  0.837  0.858  0.797  0.762  0.762  0.825  0.842
##  [8371]  0.859  0.762  0.762  0.845  0.843  0.860  0.762  0.761  0.859  0.780
##  [8381]  0.843  0.762  0.761  0.852  0.823  0.838  0.762  0.854  0.847  0.856
##  [8391]  0.762  0.815  0.849  0.840  0.761  0.822  0.827  0.830  0.762  0.826
##  [8401]  0.812  0.761  0.762  0.827  0.826  0.761  0.761  0.818  0.846  0.761
##  [8411]  0.762  0.812  0.851  0.761  0.761  0.841  1.072  0.761  0.762  0.825
##  [8421]  0.831  0.766  0.762  0.838  1.117  1.119  0.762  1.316  1.195  1.073
##  [8431]  1.233  1.367  1.512  1.272  1.201  1.280  1.487  1.206  1.136  1.311
##  [8441]  1.310  1.081  1.224  1.307  1.186  1.325  1.330  1.192  1.296  1.321
##  [8451]  1.230  0.859  1.289  0.918  0.761  0.851  0.761  0.835  0.855  0.847
##  [8461]  0.762  0.840  0.825  0.850  0.761  0.850  0.826  0.842  0.762  0.841
##  [8471]  0.830  0.851  0.761  0.856  0.826  0.861  0.762  0.762  0.853  0.836
##  [8481]  0.827  0.762  0.762  0.857  0.812  0.840  0.762  0.762  0.854  0.821
##  [8491]  0.863  0.762  0.761  0.845  0.835  0.863  0.762  0.761  0.851  0.832
##  [8501]  0.856  0.762  0.762  0.843  0.839  0.861  0.762  0.762  0.847  0.841
##  [8511]  0.763  0.762  0.762  0.856  0.850  0.772  0.762  0.762  0.830  0.837
##  [8521]  0.816  0.762  0.762  0.822  0.808  0.808  0.762  0.762  0.839  0.808
##  [8531]  0.849  0.762  0.761  0.823  0.835  0.851  0.762  0.762  0.800  0.785
##  [8541]  0.819  0.762  0.762  0.785  0.820  0.765  0.762  0.783  0.761  0.762
##  [8551]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [8561]  0.762  0.762  0.761  0.761  0.762  0.762  1.706  0.761  0.762  0.761
##  [8571]  0.762  0.761  0.762  0.762  0.762  0.761  0.761  0.762  0.762  0.762
##  [8581]  0.762  0.762  0.762  0.761  0.762  0.762  0.761  0.761  0.761  0.761
##  [8591]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8601]  0.761  0.761  0.762  0.761  0.762  0.761  0.761  0.761  0.761  0.761
##  [8611]  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761  0.761
##  [8621]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761
##  [8631]  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.761
##  [8641]  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761
##  [8651]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8661]  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761
##  [8671]  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.761  0.762  0.761
##  [8681]  0.761  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8691]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8701]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8711]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761
##  [8721]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8731]  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8741]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8751]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8761]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761
##  [8771]  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761  1.579  1.875
##  [8781]  0.761  1.100  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761
##  [8791]  0.761  0.805  0.795  0.787  0.774  0.765  0.769  0.772  0.768  0.777
##  [8801]  0.775  0.767  0.775  0.770  0.772  0.776  0.784  0.776  0.784  0.776
##  [8811]  0.784  0.768  0.770  0.775  0.775  0.770  0.768  0.773  0.771  0.771
##  [8821]  0.772  0.771  0.781  0.773  0.768  0.766  0.778  0.771  0.803  0.771
##  [8831]  0.781  0.778  0.771  0.770  0.771  0.780  0.775  0.780  0.772  0.780
##  [8841]  0.771  0.768  0.770  0.775  0.781  0.785  0.775  0.771  0.767  0.773
##  [8851]  0.771  0.770  0.774  0.773  0.772  0.769  0.770  0.766  0.768  0.775
##  [8861]  0.770  0.782  0.768  0.768  0.772  0.769  0.779  0.772  0.775  0.781
##  [8871]  0.778  0.787  0.784  0.779  0.771  0.779  0.791  0.786  0.773  0.777
##  [8881]  0.781  0.782  0.784  0.783  0.773  0.775  0.783  0.782  0.773  0.778
##  [8891]  0.774  0.768  0.771  0.768  0.780  0.776  0.782  0.794  0.785  0.781
##  [8901]  0.783  0.782  0.792  0.788  0.786  0.787  0.776  0.774  0.764  0.766
##  [8911]  0.766  0.763  0.766  0.768  0.768  0.767  0.766  0.769  0.770  0.770
##  [8921]  0.771  0.766  0.764  0.779  0.831  0.819  0.768  0.779  0.775  0.769
##  [8931]  0.770  0.767  0.767  0.770  0.767  0.767  0.769  0.769  0.769  0.765
##  [8941]  0.766  0.774  0.772  0.776  0.782  0.773  0.769  0.772  0.770  0.770
##  [8951]  0.771  0.771  0.769  0.770  0.767  0.768  0.817  0.778  0.777  0.768
##  [8961]  0.779  0.778  0.775  0.770  0.780  0.779  0.785  0.779  0.770  0.777
##  [8971]  0.772  0.776  0.783  0.781  0.775  0.774  0.776  0.771  0.785  0.797
##  [8981]  0.795  0.775  0.772  0.774  0.775  0.783  0.774  0.776  0.785  0.785
##  [8991]  0.786  0.785  0.778  0.777  0.773  0.779  0.774  0.770  0.778  0.780
##  [9001]  0.770  0.776  0.774  0.772  0.773  0.771  0.770  0.768  0.773  0.770
##  [9011]  0.770  0.773  0.774  0.765  0.769  0.766  0.766  0.777  0.777  0.773
##  [9021]  0.768  0.772  0.769  0.778  0.776  0.778  0.773  0.777  0.773  0.771
##  [9031]  0.772  0.765  0.765  0.765  0.764  0.764  0.763  0.764  0.765  0.763
##  [9041]  0.765  0.763  0.782  1.418  0.763  0.763  0.764  0.765  0.763  0.763
##  [9051]  0.764  0.763  0.766  0.763  0.764  0.766  0.765  0.769  0.764  0.765
##  [9061]  0.765  0.764  0.765  0.767  0.765  0.767  0.764  0.765  0.762  0.765
##  [9071]  0.764  0.764  0.764  0.764  0.765  0.767  0.766  0.768  0.767  0.764
##  [9081]  0.766  0.764  0.765  0.764  0.767  0.767  0.765  0.763  0.764  0.769
##  [9091]  0.770  0.766  0.763  0.767  0.765  0.764  0.763  0.764  0.768  0.765
##  [9101]  0.766  0.766  0.767  0.768  0.763  0.764  0.765  0.766  0.763  0.763
##  [9111]  0.763  0.763  0.763  0.764  0.765  0.764  0.763  0.765  0.768  0.765
##  [9121]  0.763  0.764  0.764  0.765  0.763  0.764  0.783  0.771  0.792  0.763
##  [9131]  0.773  0.778  0.776  0.767  0.767  0.766  0.766  0.764  0.770  0.768
##  [9141]  0.779  0.808  0.821  0.799  0.776  0.801  0.818  0.808  0.815  0.811
##  [9151]  0.773  0.770  0.766  0.767  0.773  0.768  0.776  0.774  0.772  0.773
##  [9161]  0.773  0.765  0.762  0.763  0.768  0.767  0.763  0.762  0.764  0.763
##  [9171]  0.763  0.767  0.765  0.764  0.763  0.766  0.763  0.765  0.763  0.764
##  [9181]  0.764  0.763  0.768  0.765  0.765  0.765  0.766  0.763  0.763  0.764
##  [9191]  0.765  0.763  0.765  0.765  0.763  0.772  0.764  0.764  0.764  0.763
##  [9201]  0.763  0.763  0.765  0.764  0.764  0.765  0.764  0.763  0.763  0.762
##  [9211]  0.762  0.762  0.762  0.762  0.763  0.762  0.763  0.764  0.763  0.763
##  [9221]  0.763  0.762  0.763  0.763  0.764  0.764  0.763  0.763  0.770  0.776
##  [9231]  0.764  0.762  0.784  0.801  0.782  0.786  0.803  0.780  0.818  0.836
##  [9241]  0.808  0.787  0.798  0.833  0.803  0.779  0.786  0.783  0.763  0.781
##  [9251]  0.763  0.779  0.772  0.772  0.784  0.781  0.771  0.774  0.794  0.790
##  [9261]  0.774  0.770  0.790  0.767  0.765  0.764  0.814  0.766  0.790  0.762
##  [9271]  0.787  0.764  0.779  0.795  0.771  0.788  0.792  0.807  0.782  0.818
##  [9281]  0.834  0.815  0.817  0.811  0.776  0.764  0.781  0.763  0.773  0.767
##  [9291]  0.772  0.763  0.762  0.785  0.768  0.776  0.771  0.763  0.762  0.762
##  [9301]  0.764  0.763  0.762  0.764  0.762  0.766  0.762  0.871  0.763  0.855
##  [9311]  0.779  0.848  0.821  0.831  0.811  0.777  0.784  0.902  0.764  0.766
##  [9321]  0.761  0.762  0.764  0.778  0.762  0.765  0.762  0.766  0.804  0.768
##  [9331]  0.768  0.784  0.767  0.765  0.777  0.774  0.780  0.800  0.762  0.761
##  [9341]  0.766  0.761  0.782  0.791  0.810  0.792  0.797  0.776  0.771  0.786
##  [9351]  0.788  0.763  0.800  0.801  0.770  0.763  0.765  0.768  0.776  0.773
##  [9361]  0.769  0.797  0.772  0.776  0.761  0.774  0.794  0.770  0.773  0.798
##  [9371]  0.761  0.765  0.807  0.805  0.768  0.772  0.809  0.805  0.762  0.770
##  [9381]  0.773  0.762  0.775  0.764  0.784  0.761  0.792  0.782  0.772  0.793
##  [9391]  0.770  0.780  0.764  0.788  0.803  0.774  0.770  0.775  0.766  0.763
##  [9401]  0.775  0.779  0.763  0.776  0.782  0.764  0.778  0.782  0.762  0.774
##  [9411]  0.790  0.762  0.762  0.762  0.762  0.771  0.762  0.767  0.762  0.766
##  [9421]  0.762  0.761  0.762  0.761  0.761  0.894  0.921  1.057  1.107  1.097
##  [9431]  1.068  1.101  1.132  1.062  0.843  0.762  0.762  0.762  0.762  0.762
##  [9441]  0.764  0.761  0.763  0.762  0.762  0.762  0.762  0.772  0.769  0.784
##  [9451]  0.770  0.770  0.769  0.774  0.761  0.761  0.761  0.763  0.763  0.763
##  [9461]  0.763  0.763  0.761  0.761  0.953  0.923  0.924  0.963  0.801  0.762
##  [9471]  0.893  0.829  0.762  0.809  0.770  0.772  0.794  0.801  0.779  0.781
##  [9481]  0.762  0.769  0.762  0.762  0.762  0.762  0.761  0.762  0.762  0.761
##  [9491]  0.761  0.761  0.761  0.761  0.776  0.773  0.771  0.776  0.771  0.783
##  [9501]  0.772  0.771  0.772  0.771  0.772  0.766  0.772  0.770  0.769  0.766
##  [9511]  0.775  0.768  0.774  0.777  0.772  1.023  0.979  0.932  0.849  0.975
##  [9521]  0.940  0.821  0.772  0.783  0.773  0.785  0.794  0.778  0.778  0.777
##  [9531]  0.778  0.780  0.792  0.814  0.798  0.883  0.849  0.835  0.848  0.902
##  [9541]  0.872  0.897  0.839  0.845  0.839  0.845  0.857  0.852  0.837  0.848
##  [9551]  0.825  0.814  0.843  0.822  0.796  0.832  0.798  0.832  0.816  0.811
##  [9561]  0.804  0.784  0.816  0.789  0.785  0.782  0.799  0.787  0.799  0.796
##  [9571]  0.789  0.780  0.775  0.796  0.837  0.761  0.789  0.876  0.766  0.797
##  [9581]  0.765  0.836  0.818  0.822  0.762  0.762  0.845  0.762  0.895  0.869
##  [9591]  0.801  0.762  0.908  0.762  0.761  0.834  0.845  0.816  0.828  0.838
##  [9601]  0.815  0.813  0.800  0.817  0.824  0.767  0.822  0.841  0.833  0.839
##  [9611]  0.829  0.828  0.837  0.845  0.821  0.777  0.766  0.764  1.217  1.524
##  [9621]  1.218  1.250  1.294  1.265  1.175  1.124  1.246  1.080  1.162  1.117
##  [9631]  1.185  1.128  1.124  1.205  1.114  1.117  1.065  2.371  2.893  2.777
##  [9641]  3.497  3.529  2.143  1.739  1.964  1.736  2.037  2.069  1.584  1.128
##  [9651]  1.096  1.189  1.185  1.904  0.761  0.761  0.761  0.761  0.761  0.761
##  [9661]  1.417  1.860  0.761  0.761  6.832  8.015  7.050  6.124  4.640  2.892
##  [9671]  0.878  0.876  0.874  0.863  0.872  0.832  0.864  0.853  0.863  0.872
##  [9681]  0.865  0.877  0.860  0.886  1.523  1.629  1.661  1.664  1.489  0.831
##  [9691]  0.775  0.781  0.819  0.786  0.882  0.904  0.855  0.769  0.886  0.886
##  [9701]  0.855  0.776  0.808  0.793  0.765  0.766  0.804  0.805  0.761  0.761
##  [9711]  0.761  0.761  0.762  0.761  0.761  0.762  0.764  0.762  0.761  0.789
##  [9721]  0.762  0.771  0.762  0.764  0.789  0.857  0.766  0.774  0.761  0.765
##  [9731]  0.808  0.805  0.761  0.924  0.774  0.836  0.870  0.891  0.814  1.174
##  [9741]  0.999  1.064  0.935  1.033  0.774  0.810  0.795  0.761  0.794  0.824
##  [9751]  0.854  0.767  0.770  0.767  1.012  0.970  1.046  1.048  0.849  1.111
##  [9761]  1.087  1.114  1.127  0.830  0.870  0.891  1.150  0.810  0.839  0.811
##  [9771]  0.871  0.824  0.863  1.388  0.842  0.887  0.988  0.778  0.855  0.800
##  [9781]  0.798  0.826  0.797  0.812  0.875  0.824  0.807  0.811  0.899  0.857
##  [9791]  0.782  0.971  0.780  0.927  0.862  0.782  0.859  0.779  0.917  0.782
##  [9801]  0.950  0.778  0.940  0.778  0.977  0.778  0.785  1.448  1.344  1.125
##  [9811]  1.122  1.142  1.112  1.162  1.113  1.096  1.140  2.148  0.843  0.858
##  [9821]  0.824  0.792  0.819  0.806  0.839  0.865  0.789  0.794  0.792  0.789
##  [9831]  0.784  0.787  0.787  0.799  0.785  0.781  0.796  0.791  0.783  0.809
##  [9841]  0.761  1.526  1.186  1.128  1.350  1.177  1.208  1.252  1.279  1.095
##  [9851]  1.114  1.049  1.083  1.176  1.092  1.030  0.961  0.932  1.213  1.155
##  [9861]  1.106  1.146  1.200  1.082  1.176  0.979  0.982  0.989  1.126  1.007
##  [9871]  0.939  1.108  1.028  0.973  0.818  0.785  1.051  1.215  1.410  0.861
##  [9881]  1.265  1.250  1.317  1.052  1.243  1.173  1.053  1.058  1.222  1.268
##  [9891]  1.275  1.532  1.964  1.028  1.464  0.824  0.830  1.199  0.792  0.848
##  [9901]  0.798  1.271  1.131  0.785  1.458  1.270  1.243  1.237  1.078  0.999
##  [9911]  0.861  1.003  1.017  1.054  0.977  1.019  1.014  1.024  1.113  1.109
##  [9921]  1.159  0.896  0.801  0.852  1.889  0.950  1.222  1.081  1.017  1.086
##  [9931]  0.806  0.915  1.240  1.116  1.240  1.173  1.092  1.118  1.030  0.989
##  [9941]  0.761  1.361  1.242  1.258  0.837  0.839  0.772  1.159  1.049  1.055
##  [9951]  1.261  0.988  0.864  0.869  0.992  0.828  0.825  0.768  0.796  0.818
##  [9961]  0.858  0.787  0.895  0.778  0.860  0.828  0.822  0.761  0.761  0.761
##  [9971]  0.761  0.761  0.761  1.239  1.376  1.150  1.438  1.136  1.178  1.317
##  [9981]  1.869  1.414  1.448  1.554  0.999  1.199  1.166  1.204  1.120  1.237
##  [9991]  1.249  1.316  1.255  1.303  1.296  1.253  1.329  1.281  1.112  1.077
## [10001]  1.409  1.277  1.321  0.866  0.816  0.837  0.927  0.897  0.913  0.889
## [10011]  0.871  0.866  0.872  0.825  0.854  0.926  0.973  0.991  0.865  0.826
## [10021]  0.857  0.893  0.941  0.904  0.873  0.947  0.969  0.963  0.955  0.924
## [10031]  0.904  0.927  0.988  0.902  0.914  1.002  0.973  0.762  1.175  1.198
## [10041]  1.102  1.062  0.914  0.986  0.935  1.162  0.761  0.811  0.801  0.789
## [10051]  1.225  1.219  2.179  1.704  1.797  1.455  1.561  0.951  1.825  1.100
## [10061]  0.948  1.287  0.952  0.964  1.250  1.047  1.173  1.062  1.132  1.180
## [10071]  1.012  1.102  1.030  1.103  1.018  1.020  1.011  1.018  0.761  1.002
## [10081]  0.762  1.032  0.761  0.962  0.762  1.018  0.761  1.034  0.761  1.219
## [10091]  0.761  1.012  0.761  1.023  0.764  1.028  0.761  1.389  0.761  1.837
## [10101]  0.764  1.324  0.762  1.138  0.762  2.038  0.761  1.770  0.761  1.444
## [10111]  0.761  1.779  0.761  1.405  0.761  1.451  0.761  1.421  1.919  0.813
## [10121]  1.040  0.818  0.825  0.904  0.885  1.365  0.967  0.935  0.967  1.258
## [10131]  1.229  1.021  1.220  1.330  1.225  0.971  1.302  1.120  1.078  1.079
## [10141]  0.950  0.779  0.975  1.367  1.502  1.486  1.660  1.635  1.273  1.469
## [10151]  1.376  1.212  0.836  0.893  1.264  1.243  0.913  1.328  1.287  1.259
## [10161]  0.910  0.877  1.283  1.207  1.113  1.307  1.296  1.203  0.990  1.805
## [10171]  1.342  1.404  1.312  1.313  1.307  1.251  0.834  0.827  0.806  0.797
## [10181]  1.230  1.362  1.354  1.347  1.172  0.889  0.826  0.829  0.860  0.761
## [10191]  0.761  0.761  0.761  0.761  1.052  1.138  1.174  0.761  0.954  0.761
## [10201]  1.091  0.761  1.304  0.762  1.332  0.761  1.221  0.761  1.227  1.226
## [10211]  1.069  1.105  1.244  1.260  1.213  1.203  0.761  0.787  0.761  0.917
## [10221]  0.979  0.821  0.762  0.763  0.761  0.763  0.761  0.761  0.762  0.762
## [10231]  0.761  0.762  0.762  0.761  0.762  0.761  0.762  0.761  0.761  0.761
## [10241]  0.761  0.761  0.761  0.761  0.771  0.896  1.170  0.983  0.943  0.923
## [10251]  0.775  0.872  1.093  0.937  0.765  1.131  1.015  1.139  0.789  0.949
## [10261]  0.966  0.890  0.977  0.788  0.845  0.838  0.775  0.842  0.799  0.866
## [10271]  1.224  0.861  1.121  0.761  0.813  0.816  0.800  0.772  0.779  0.818
## [10281]  0.833  0.792  0.815  0.781  0.797  0.793  0.795  0.812  0.794  0.881
## [10291]  0.802  0.941  0.802  0.861  0.813  0.883  0.805  0.874  0.795  0.838
## [10301]  0.846  0.850  0.785  0.844  0.798  0.831  0.824  0.847  0.810  0.965
## [10311]  0.847  0.778  0.778  0.775  0.821  0.786  0.767  0.780  0.777  0.767
## [10321]  0.763  0.766  0.772  0.761  0.778  0.812  0.776  0.769  0.773  0.779
## [10331]  0.783  0.809  0.796  0.824  0.799  0.800  0.792  0.813  0.810  1.019
## [10341]  1.093  1.270  1.301  1.196  1.106  1.164  1.044  1.105  1.090  1.301
## [10351]  2.025  1.243  1.420  1.322  1.484  1.428  1.365  1.372  1.153  2.226
## [10361]  1.458  1.259  1.416  1.473  1.637  1.473  1.103  1.278  1.342  0.897
## [10371]  1.229  1.146  0.857  1.270  1.418  1.738  1.216  1.363  1.037  1.272
## [10381]  1.286  1.402  1.827  1.428  1.750  1.723  1.581  1.604  1.604  1.894
## [10391]  1.995  1.423  1.451  1.255  1.147  1.276  1.275  1.369  1.504  1.368
## [10401]  1.308  1.401  1.615  1.083  1.528  0.988  1.191  1.636  1.570  1.120
## [10411]  0.803  0.787  0.769  0.765  0.766  0.776  0.792  0.761  0.767  0.848
## [10421]  0.761  1.016  0.764  1.134  0.864  1.127  0.890  1.061  0.826  0.986
## [10431]  1.685  1.785  0.776  1.272  1.350  1.338  0.876  1.118  0.868  0.767
## [10441]  0.783  0.786  0.780  0.795  0.855  0.849  0.802  0.868  0.885  0.817
## [10451]  0.815  0.775  0.762  0.761  1.882  1.703  1.134  1.005  1.445  1.317
## [10461]  1.073  0.995  1.266  1.359  1.179  1.147  1.151  1.149  1.229  1.498
## [10471]  1.287  1.052  1.025  0.897  1.121  1.203  1.252  1.171  1.236  1.142
## [10481]  1.338  1.094  0.889  0.996  1.031  0.851  0.972  1.057  1.310  1.269
## [10491]  0.992  1.095  1.192  1.143  1.203  1.113  1.190  0.899  1.145  1.183
## [10501]  1.249  1.258  1.115  1.043  1.013  1.102  1.172  1.156  0.992  0.989
## [10511]  0.765  0.841  0.928  0.767  0.806  1.429  0.765  0.765  0.762  1.493
## [10521]  0.761  0.761  0.765  0.762  0.762  0.762  0.765  0.761  0.761  0.944
## [10531]  0.761  0.765  0.765  1.641  2.219  1.550  0.975  0.797  0.785  0.989
## [10541]  0.878  0.906  1.651  1.645  1.966  1.408  0.815  0.769  0.769  0.769
## [10551]  0.765  0.774  0.766  0.870  1.534  0.885  0.765  0.780  0.789  0.812
## [10561]  0.856  0.912  0.841  0.799  0.893  0.925  1.018  0.960  0.972  1.124
## [10571]  1.021  0.977  1.094  0.839  0.917  0.853  0.788  0.861  1.023  1.180
## [10581]  1.063  1.260  1.137  1.321  1.315  1.150  0.924  0.864  0.839  0.876
## [10591]  0.913  1.017  0.922  1.050  1.425  1.082  0.928  1.006  0.866  1.098
## [10601]  1.070  1.051  1.116  1.028  1.080  1.202  0.863  1.057  1.046  1.774
## [10611]  1.581  2.157  2.379  2.570  1.447  1.583  1.186  1.055  1.326  1.479
## [10621]  1.294  1.280  0.940  0.998  1.038  1.049  1.053  0.972  0.763  0.763
## [10631]  0.763  0.763  0.763  0.761  0.762  0.761  0.763  0.763  0.763  0.763
## [10641]  0.763  0.763  0.763  0.763  0.763  0.763  0.763  0.901  0.761  0.830
## [10651]  0.761  0.950  0.761  0.980  0.763  1.051  0.763  1.608  0.761  1.870
## [10661]  0.761  2.792  0.761  1.755  0.761  1.198  0.761  1.422  0.762  1.572
## [10671]  0.761  1.519  0.761  1.188  0.761  1.050  0.761  1.063  0.761  0.980
## [10681]  0.762  1.695  0.761  1.311  0.761  1.598  0.761  0.763  0.761  0.761
## [10691]  0.761  0.761  0.761  0.763  0.761  0.761  0.761  0.761  0.761  0.761
## [10701]  0.761  0.761  0.761  0.763  0.761  0.761  0.761  0.763  0.763  0.763
## [10711]  0.998  1.213  1.565  2.601  2.785  2.281  1.741  2.324  2.323  1.992
## [10721]  1.904  2.449  1.937  3.336  0.761  0.931  1.191  1.260  0.965  1.101
## [10731]  0.874  1.063  1.069  0.868  1.179  1.137  1.197  0.915  1.016  1.087
## [10741]  1.055  1.715  1.477  1.058  1.227  1.099  0.997  1.712  2.001  1.234
## [10751]  1.228  1.255  1.363  1.442  1.400  1.667  1.858  1.729  1.949  2.110
## [10761]  1.735  1.604  1.691  1.707  1.642  1.365  1.700  1.678  0.764  0.761
## [10771]  0.761  1.539  1.157  0.761  1.116  0.798  1.035  0.873  0.913  0.880
## [10781]  0.761  0.761  0.761  0.929  1.014  0.761  1.082  1.039  0.978  1.055
## [10791]  1.075  1.830  1.072  1.085  0.978  1.054  0.971  0.944  0.901  0.986
## [10801]  1.002  1.002  1.051  1.066  1.068  1.379  1.352  1.011  0.761  0.873
## [10811]  0.761  0.761  0.790  1.331  1.467  1.218  2.316  1.800  0.989  1.565
## [10821]  1.328  1.732  1.615  1.373  1.619  1.503  1.270  1.800  1.705  1.491
## [10831]  1.080  0.994  1.002  1.130  0.762  0.761  0.761  0.833  0.804  1.279
## [10841]  1.112  1.035  1.106  1.241  1.232  2.087  2.668  2.327  1.754  1.709
## [10851]  1.430  1.938  1.247  1.903  2.341  1.692  1.778  1.609  1.493  1.445
## [10861]  1.974  1.294  1.178  0.761  0.761  0.761  0.761  0.762  1.089  0.915
## [10871]  1.176  1.159  1.143  3.370  1.154  1.119  1.108  1.034  1.193  1.169
## [10881]  1.191  1.140  1.293  1.196  1.977  0.964  2.504  3.531  1.736  1.425
## [10891]  1.281  1.271  1.960  1.385  1.280  1.433  2.544  1.486  1.857  1.516
## [10901]  1.946  1.397  1.577  1.600  1.731  1.236  1.503  1.758  1.345  1.403
## [10911]  2.263  1.596  2.161  1.299  1.622  1.398  1.833  0.951  1.694  1.439
## [10921]  1.950  0.773  1.871  1.296  1.645  0.878  2.165  2.428  2.134  1.255
## [10931]  1.378  1.362  1.370  1.319  1.404  1.283  1.275  1.921  1.764  1.422
## [10941]  0.768  0.761  0.818  0.761  0.964  0.894  0.773  0.854  0.957  0.761
## [10951]  0.761  0.899  2.089  0.965  1.390  0.992  1.018  1.009  1.008  1.040
## [10961]  1.169  0.989  1.307  0.991  1.398  0.914  1.034  1.030  1.000  1.285
## [10971]  1.259  1.310  1.277  1.186  1.170  1.300  1.190  1.164  1.517  0.984
## [10981]  0.986  0.925  0.879  0.866  0.906  0.823  0.982  0.779  1.049  0.801
## [10991]  0.783  1.148  1.081  1.145  0.800  1.416  0.761  1.160  1.159  0.992
## [11001]  0.965  0.765  0.779  0.763  0.798  1.071  1.077  2.215  1.558  1.902
## [11011]  2.691  1.651  1.862  2.102  1.783  1.780  1.768  2.278  2.009  1.779
## [11021]  1.898  1.662  1.673  1.808  2.003  1.925  1.760  2.487  2.063  0.933
## [11031]  1.466  1.186  1.134  1.133  0.978  1.446  1.193  1.657  1.402  1.082
## [11041]  1.276  1.058  1.026  1.036  1.159  1.227  0.958  1.054  1.183  1.177
## [11051]  1.241  1.027  0.837  0.949  0.920  0.911  1.037  0.938  0.963  0.924
## [11061]  1.065  1.109  1.087  1.129  1.135  1.076  1.050  0.920  0.934  0.998
## [11071]  1.006  1.063  0.875  0.965  0.980  0.843  1.036  0.907  1.284  0.761
## [11081]  1.451  1.386  1.316  1.193  0.987  1.148  1.029  1.089  1.366  1.531
## [11091]  1.029  1.425  1.048  1.072  0.818  0.761  1.042  1.089  1.105  0.914
## [11101]  1.048  1.106  1.055  0.995  1.044  0.990  1.043  1.066  0.761  0.770
## [11111]  0.826  0.817  0.814  0.793  0.898  1.343  0.852  0.818  1.573  1.574
## [11121]  0.985  1.003  0.761  1.554  0.845  0.939  1.103  1.553  1.658  1.038
## [11131]  1.675  0.845  1.086  0.999  1.294  1.414  0.896  1.503  0.917  1.430
## [11141]  1.546  1.141  1.507  1.466  1.175  0.947  0.761  0.761  1.339  0.881
## [11151]  0.908  1.619  1.882  0.887  0.810  1.218  1.188  1.011  0.953  1.030
## [11161]  1.140  1.074  1.033  0.794  1.169  0.855  0.912  0.905  0.824  0.796
## [11171]  0.856  0.852  0.802  0.786  0.784  0.817  0.833  0.761  0.761  0.958
## [11181]  1.004  1.145  0.765  0.790  0.945  1.144  0.765  0.765  0.762  0.765
## [11191]  1.557  1.243  1.404  1.339  0.762  0.822  0.946  1.224  1.140  1.135
## [11201]  1.235  0.823  1.486  1.229  1.302  1.443  1.791  2.525  1.741  2.043
## [11211]  1.317  1.255  1.251  2.190  0.981  1.362  1.389  1.386  1.232  0.765
## [11221]  0.763  0.766  0.765  0.763  0.763  0.763  0.784  0.891  0.897  1.300
## [11231]  0.962  0.945  1.087  0.924  1.045  1.044  1.003  0.871  0.918  0.895
## [11241]  0.864  0.848  0.974  0.855  0.762  0.762  0.762  0.762  0.761  0.816
## [11251]  0.761  0.768  0.762  0.762  0.762  0.779  0.765  2.671  0.762  2.155
## [11261]  0.762  1.663  0.766  1.628  0.761  1.555  1.612  1.739  1.623  1.703
## [11271]  1.712  1.164  1.150  1.403  1.347  0.762  1.428  0.761  0.762  0.761
## [11281]  0.761  0.761  0.762  0.762  0.761  0.762  0.762  0.762  0.940  1.543
## [11291]  1.549  0.775  0.871  1.628  0.789  0.810  0.765  0.762  0.765  0.761
## [11301]  0.976  0.880  0.867  0.857  0.761  0.785  0.794  0.925  1.284  1.200
## [11311]  1.315  0.785  1.675  1.396  1.580  0.835  0.851  0.813  0.805  0.795
## [11321]  0.761  0.763  1.542  1.569  1.518  0.833  0.792  0.848  0.770  1.602
## [11331]  1.591  0.834  1.415  0.781  0.805  1.521  1.604  0.815  1.587  0.851
## [11341]  1.368  1.510  0.927  1.633  0.790  0.868  0.828  0.809  0.771  0.800
## [11351]  0.776  0.861  0.853  0.977  0.901  0.799  0.860  0.827  0.879  0.815
## [11361]  0.879  0.827  0.925  0.821  0.852  0.837  0.795  0.846  0.879  0.853
## [11371]  0.877  0.862  0.767  0.781  0.765  0.769  0.798  0.779  0.791  0.855
## [11381]  0.953  0.830  0.765  0.762  0.767  0.765  0.771  0.765  0.763  0.770
## [11391]  0.773  0.766  0.766  0.767  0.763  0.762  0.764  0.763  0.769  0.765
## [11401]  0.767  0.768  0.768  0.771  0.775  0.770  0.805  0.807  0.761  0.761
## [11411]  0.866  0.769  0.801  0.782  0.763  0.765  0.763  0.771  0.768  0.779
## [11421]  0.770  0.816  0.816  0.771  0.779  0.793  0.802  0.815  0.771  0.768
## [11431]  0.776  0.770  0.783  0.803  1.051  0.856  0.795  0.788  0.930  1.288
## [11441]  0.761  0.760  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [11451]  0.761  0.787  0.761  0.870  0.761  0.761  0.761  0.761  0.761  1.048
## [11461]  1.480  2.654  1.225  0.884  0.824  1.002  0.955  0.784  0.922  0.936
## [11471]  0.881  1.396  0.908  0.963  1.158  1.033  0.940  0.945  0.789  0.863
## [11481]  0.847  0.794  0.828  0.797  0.782  0.824  1.502  2.009  1.254  1.455
## [11491]  1.512  1.172  2.251  1.504  0.793  0.764  0.796  0.762  0.761  0.776
## [11501]  0.761  0.761  0.762  0.762  0.761  0.761  0.761  0.761  0.761  0.761
## [11511]  0.761  0.761  0.761  0.824  0.775  0.793  0.807  0.783  0.774  0.783
## [11521]  0.764  0.819  0.766  0.766  0.811  0.799  0.762  0.808  0.766  0.775
## [11531]  0.763  0.762  0.857  0.762  0.761  0.763  0.769  0.766  0.762  1.059
## [11541]  0.828  0.891  0.943  0.861  0.869  1.290  1.971  0.828  0.783  0.764
## [11551]  0.778  0.806  0.833  0.830  0.867  0.875  0.835  0.815  0.867  0.870
## [11561]  0.984  0.842  1.093  0.999  0.935  0.829  0.896  0.955  1.480  3.972
## [11571]  0.761  0.761  0.761  0.982  0.761  0.866  0.907  0.954  0.931  0.898
## [11581]  0.899  1.119  0.877  0.825  0.956  0.781  0.803  0.802  0.777  0.890
## [11591]  0.783  0.960  0.762  0.761  0.761  0.761  1.959  1.246  1.686  1.147
## [11601]  1.444  1.291  1.252  1.302  0.874  0.836  0.896  0.806  0.778  0.782
## [11611]  0.774  0.779  1.029  1.133  1.098  1.243  1.305  1.159  1.270  1.115
## [11621]  1.682  1.453  0.762  0.797  0.764  0.771  0.851  0.927  0.904  1.462
## [11631]  1.910  1.935  0.761  0.889  1.071  0.892  1.110  0.943  0.761  0.762
## [11641]  0.761  0.761  0.761  0.761  1.293  1.521  1.285  1.340  1.341  1.335
## [11651]  1.726  1.315  0.761  1.319  1.405  1.612  1.191  1.177  1.291  1.327
## [11661]  1.180  1.297  1.182  1.132  1.379  1.363  1.372  1.237  1.487  1.361
## [11671]  1.348  0.761  0.761  0.761  0.761  0.875  0.931  1.315  1.131  1.130
## [11681]  1.145  0.777  4.348  0.956  1.035  0.832  0.983  2.294  1.209  1.706
## [11691]  2.326  0.933  1.284  1.367  2.714  1.410  2.672  1.435  1.113  1.812
## [11701]  1.614  0.762  0.768  0.768  0.765  0.841  0.879  0.890  0.799  0.766
## [11711]  0.820  0.769  0.781  0.794  0.771  0.763  0.774  0.777  0.783  0.777
## [11721]  0.773  0.793  0.799  0.782  0.781  0.786  0.774  0.772  0.848  0.977
## [11731]  0.847  0.927  0.762  0.994  0.762  0.761  0.853  0.761  0.775  0.763
## [11741]  0.916  0.851  1.599  1.551  1.084  0.787  0.832  0.769  0.928  0.768
## [11751]  2.258  2.491  0.761  0.761  1.158  0.904  0.763  0.766  0.959  1.584
## [11761]  0.974  1.073  1.082  0.762  0.761  0.891  1.027  1.132  1.169  0.761
## [11771]  0.761  1.027  0.761  1.208  1.199  1.160  1.170  1.021  0.761  0.761
## [11781]  1.012  0.899  0.891  1.007  0.977  0.817  0.884  0.867  0.947  0.775
## [11791]  0.823  0.794  0.863  0.912  0.788  0.831  0.784  0.775  0.791  0.925
## [11801]  1.390  1.202  0.818  0.761  0.761  0.762  0.761  0.761  0.761  0.762
## [11811]  0.761  0.841  0.761  0.761  0.761  0.954  0.761  0.761  0.761  0.761
## [11821]  1.169  1.158  1.341  1.279  1.752  1.414  4.375  2.072  1.672  1.676
## [11831]  0.925  0.828  0.857  0.936  0.795  0.834  0.826  0.789  0.887  0.792
## [11841]  0.788  0.889  0.918  1.068  1.104  0.933  0.761  0.761  0.869  0.761
## [11851]  0.786  0.832  0.891  0.834  0.869  0.852  0.867  1.229  0.840  0.970
## [11861]  1.280  0.822  1.438  0.762  0.762  0.767  0.770  0.762  0.763  0.777
## [11871]  0.765  0.787  0.769  0.770  0.762  0.763  0.802  0.775  0.780  0.899
## [11881]  1.193  1.507  1.087  0.762  0.769  0.761  0.765  0.789  0.761  0.762
## [11891]  0.816  0.919  0.904  0.926  0.979  0.915  1.020  1.061  0.888  0.822
## [11901]  1.050  0.817  1.077  0.983  2.416  1.644  0.962  0.934  0.881  0.889
## [11911]  0.935  1.056  0.937  0.898  0.869  0.875  0.868  1.736  0.778  0.828
## [11921]  0.932  0.854  0.851  0.960  0.844  0.980  1.033  1.286  1.025  1.380
## [11931]  0.982  0.989  0.949  0.849  0.900  1.031  1.015  0.856  0.984  2.309
## [11941]  0.901  1.059  1.136  4.256  2.977  0.978  0.795  1.020  0.989  1.041
## [11951]  1.159  1.155  0.790  1.207  1.277  1.429  1.399  0.892  1.212  0.852
## [11961]  1.065  0.905  0.860  0.763  0.762  0.787  0.788  0.788  0.790  0.793
## [11971]  0.761  0.822  0.808  0.788  0.783  0.775  0.766  0.792  0.765  0.778
## [11981]  0.767  0.780  0.784  0.800  0.790  0.935  0.853  0.810  0.796  0.819
## [11991]  0.779  0.789  1.116  0.893  0.867  0.829  1.008  1.012  1.229  1.221
## [12001]  0.999  1.103  0.799  0.773  0.779  0.797  0.801  0.882  0.901  0.841
## [12011]  1.044  1.035  0.913  0.905  0.879  1.024  1.074  2.863  3.173  2.860
## [12021]  2.710  0.835  0.852  1.227  0.859  1.234  0.821  0.826  2.447  1.498
## [12031]  0.761  0.817  0.761  1.939  1.277  1.324  0.762  0.799  0.792  1.006
## [12041]  0.761  0.828  0.853  0.761  0.776  1.049  1.063  2.774  2.038  0.762
## [12051]  0.933  0.968  0.914  0.762  0.762  0.911  1.448  1.097  0.776  0.774
## [12061]  0.770  0.768  0.775  0.772  0.772  0.773  0.769  0.768  0.765  0.764
## [12071]  0.765  0.764  0.764  0.763  0.764  0.763  0.763  0.764  0.764  0.789
## [12081]  0.779  0.782  0.767  0.777  0.786  1.067  1.096  0.994  1.096  1.119
## [12091]  1.279  1.320  1.054  1.130  1.213  1.227  1.027  0.876  1.169  1.112
## [12101]  1.017  0.941  0.916  0.816  0.766  0.762  0.843  1.130  0.838  0.870
## [12111]  0.858  0.892  0.811  1.926  0.761  1.945  1.988  1.633  1.496  1.375
## [12121]  1.524  1.410  1.524  1.474  1.410  2.082  1.507  2.113  2.174  1.551
## [12131]  1.431  1.810  1.412  1.124  1.097  1.287  0.761  0.761  0.761  0.761
## [12141]  0.761  0.761  0.761  1.120  0.761  0.762  0.774  0.774  0.798  0.932
## [12151]  1.232  1.475  1.454  0.947  0.985  0.841  0.838  1.171  1.229  1.370
## [12161]  0.763  0.762  0.762  0.762  0.762  0.763  0.764  0.762  0.763  0.762
## [12171]  0.762  0.762  0.764  0.766  0.764  0.765  0.765  0.763  0.763  0.769
## [12181]  0.765  0.766  0.770  0.769  0.763  0.766  0.769  0.766  0.761  0.762
## [12191]  0.761  0.762  0.761  0.761  0.764  0.765  0.762  0.857  0.764  0.762
## [12201]  0.800  0.767  0.795  0.830  0.767  0.792  0.761  0.793  0.762  0.762
## [12211]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.770  0.767  0.911
## [12221]  0.815  0.907  0.792  0.857  0.762  0.807  0.771  0.770  0.763  0.785
## [12231]  0.769  0.845  0.789  0.810  0.883  0.836  0.860  0.800  0.764  0.774
## [12241]  0.781  0.768  0.763  0.956  0.813  0.846  0.827  0.784  1.048  1.142
## [12251]  1.159  0.875  0.936  0.993  0.809  0.785  0.793  0.804  0.783  0.807
## [12261]  0.858  0.822  0.809  0.856  0.922  0.941  0.836  0.970  1.009  0.890
## [12271]  0.882  0.795  0.761  0.762  0.761  0.798  0.761  0.761  0.761  0.761
## [12281]  0.761  0.761  0.761  0.761  0.761  0.881  0.761  0.764  0.768  0.766
## [12291]  0.763  0.761  0.761  0.768  0.767  0.767  0.778  0.772  0.771  0.764
## [12301]  0.765  0.776  0.766  0.762  0.761  1.601  1.415  1.111  1.231  1.371
## [12311]  3.307  2.694  1.931  1.930  0.944  0.803  0.787  0.783  0.767  0.767
## [12321]  2.112  0.816  0.950  0.761  0.998  0.762  0.774  0.763  1.515  1.567
## [12331]  0.826  1.359  1.453  0.857  1.411  1.625  1.600  1.391  0.873  1.374
## [12341]  1.874  1.684  1.581  1.239  1.140  1.173  1.355  1.095  0.761  0.871
## [12351]  0.934  1.050  1.098  1.048  1.060  0.761  0.761  0.761  0.828  0.835
## [12361]  0.975  1.064  0.761  0.781  0.761  0.802  0.801  0.900  0.894  0.867
## [12371]  0.891  0.785  1.283  2.674  1.047  1.007  1.062  1.217  1.164  2.288
## [12381]  0.761  0.761  0.762  0.769  0.766  0.878  0.957  1.034  0.834  0.826
## [12391]  0.930  0.761  0.762  0.786  0.762  0.813  0.818  0.762  0.773  0.762
## [12401]  0.817  0.936  0.958  0.874  0.822  0.793  0.789  0.886  0.918  3.084
## [12411]  4.436  0.899  0.799  0.766  0.772  1.051  1.124  0.827  1.044  0.958
## [12421]  0.794  0.790  0.838  0.838  0.796  0.827  0.840  0.837  0.790  0.844
## [12431]  0.765  0.762  0.761  0.761  0.761  0.761  0.763  0.762  0.761  0.761
## [12441]  0.761  0.767  0.792  0.761  0.761  0.761  0.761  0.762  0.762  0.761
## [12451]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.908  0.761
## [12461]  0.761  1.439  1.219  1.494  0.763  1.411  0.763  0.763  0.892  0.846
## [12471]  1.121  1.090  1.449  1.199  1.029  1.178  0.763  0.762  0.764  0.766
## [12481]  0.765  0.764  0.764  0.763  0.764  0.761  0.761  0.761  0.761  0.761
## [12491]  0.761  0.762  0.765  0.762  0.761  0.761  0.762  0.762  0.761  0.762
## [12501]  0.764  0.763  0.761  0.816  2.833  4.152  2.977  2.133  0.761  0.761
## [12511]  0.761  0.761  1.278  1.213  1.262  0.761  0.860  1.234  1.316  0.869
## [12521]  0.869  0.805  0.791  1.261  1.298  1.112  0.764  0.764  0.873  0.766
## [12531]  0.763  0.873  1.106  1.173  1.105  0.842  1.149  0.992  0.867  0.818
## [12541]  1.100  0.830  0.798  0.813  0.804  0.829  0.917  0.826  0.825  0.818
## [12551]  1.202  0.828  0.808  0.809  0.823  0.852  0.805  0.800  0.787  0.804
## [12561]  0.814  0.785  0.798  0.804  0.787  0.809  0.816  0.824  0.802  0.761
## [12571]  1.036  0.966  0.955  1.047  1.034  1.009  1.032  0.762  0.762  0.762
## [12581]  0.762  1.121  0.764  0.766  0.763  0.816  0.761  0.761  0.761  0.761
## [12591]  0.761  0.791  0.863  0.839  0.842  0.834  0.814  0.847  0.797  0.802
## [12601]  0.767  0.858  0.802  0.824  0.812  0.856  0.813  0.808  0.789  0.800
## [12611]  0.777  0.771  0.773  0.779  0.786  0.781  0.795  0.797  0.789  0.811
## [12621]  0.894  0.762  0.784  0.781  0.784  0.807  0.784  0.762  0.789  0.762
## [12631]  1.120  0.815  0.801  0.795  0.792  0.792  0.797  0.791  0.789  0.782
## [12641]  0.794  0.802  0.818  0.802  0.808  0.837  1.045  0.785  0.795  0.790
## [12651]  0.790  0.787  1.174  0.866  1.159  0.858  0.836  1.177  0.797  1.009
## [12661]  0.831  1.186  1.098  1.002  0.824  0.856  0.835  0.864  0.878  0.889
## [12671]  0.850  0.808  0.996  0.777  0.788  0.778  0.792  0.793  0.787  0.787
## [12681]  0.783  0.771  0.784  0.776  0.828  0.792  0.781  0.805  0.828  0.805
## [12691]  0.805  0.773  1.087  0.779  1.075  0.773  0.915  0.770  0.960  0.799
## [12701]  1.032  0.802  0.948  0.805  0.899  0.799  0.835  0.788  0.835  0.829
## [12711]  0.821  0.817  0.805  0.826  0.831  0.911  0.806  0.807  0.792  0.784
## [12721]  0.783  1.067  0.761  0.761  0.761  0.761  0.809  0.801  0.761  0.762
## [12731]  0.835  0.789  0.761  0.786  1.019  0.780  0.787  0.799  0.819  0.819
## [12741]  0.819  0.798  0.782  0.778  0.782  0.791  0.780  0.788  0.788  0.791
## [12751]  0.777  0.776  0.776  1.052  0.761  0.761  0.761  0.762  0.793  0.766
## [12761]  0.813  0.840  1.095  1.165  0.838  1.071  0.823  0.974  0.983  0.904
## [12771]  0.812  0.822  0.798  0.843  0.805  0.784  0.789  0.797  0.793  0.795
## [12781]  0.782  0.787  0.783  0.783  0.786  0.781  0.773  0.796  0.792  0.863
## [12791]  0.843  0.848  0.812  0.826  0.838  0.816  0.800  0.816  0.817  0.804
## [12801]  0.823  0.818  0.870  0.845  0.853  0.829  0.797  0.784  1.093  0.764
## [12811]  0.761  0.775  0.764  0.811  0.813  0.793  0.833  0.846  0.828  0.823
## [12821]  0.849  0.853  0.812  0.808  0.849  0.838  0.841  0.841  0.849  0.858
## [12831]  0.858  0.849  0.860  0.767  0.808  1.056  0.761  0.761  0.773  0.784
## [12841]  0.807  0.808  0.795  0.794  0.835  0.796  0.817  0.849  0.832  1.148
## [12851]  0.816  1.061  1.103  1.106  1.117  0.815  1.088  0.779  1.079  1.193
## [12861]  0.848  1.014  0.788  0.800  0.803  0.787  0.779  0.843  0.797  0.799
## [12871]  0.809  0.891  0.844  0.812  0.794  0.814  0.818  0.886  0.788  0.830
## [12881]  0.793  0.803  0.780  0.774  0.768  0.798  0.793  0.776  0.816  0.790
## [12891]  0.795  0.778  0.797  0.763  0.799  0.792  1.074  0.951  0.895  1.148
## [12901]  1.098  1.139  1.176  1.272  1.183  1.221  0.836  0.780  0.788  0.766
## [12911]  0.831  0.798  0.822  0.951  0.842  0.817  0.817  1.182  0.799  0.816
## [12921]  0.893  0.807  0.799  0.815  0.912  0.956  1.014  0.845  0.930  0.955
## [12931]  0.920  0.835  0.828  0.812  0.821  0.854  0.922  0.871  0.874  0.849
## [12941]  0.900  0.846  0.830  0.859  0.823  0.861  0.843  0.816  0.834  0.829
## [12951]  0.789  0.762  0.764  0.774  0.764  0.764  0.763  0.839  0.781  0.785
## [12961]  0.789  0.791  0.789  0.784  0.776  0.776  0.772  0.796  0.787  0.792
## [12971]  0.787  0.799  0.774  0.779  0.768  0.762  0.763  0.767  0.763  0.762
## [12981]  0.762  0.761  0.766  0.763  0.825  0.862  0.887  0.903  0.857  0.865
## [12991]  0.872  0.803  0.810  0.806  0.833  0.810  0.808  0.814  0.823  0.836
## [13001]  0.877  0.827  0.785  0.788  0.804  0.800  0.797  0.772  0.812  0.819
## [13011]  0.831  0.814  0.836  0.980  0.823  0.994  0.936  0.908  0.985  0.942
## [13021]  0.919  0.888  0.873  0.909  0.887  0.876  0.910  0.828  0.811  0.829
## [13031]  0.864  0.812  0.889  0.832  0.864  0.823  0.895  0.901  0.853  0.821
## [13041]  0.807  0.778  0.789  0.794  0.784  0.782  0.791  0.889  0.873  0.845
## [13051]  0.837  0.854  0.816  0.899  0.807  0.815  0.841  0.857  0.829  0.815
## [13061]  0.845  0.851  0.862  0.856  0.841  0.776  0.810  0.771  0.789  0.780
## [13071]  0.804  0.787  0.803  0.788  1.049  0.833  0.920  0.825  0.823  0.848
## [13081]  0.792  0.885  0.940  0.885  0.931  0.924  0.895  0.880  0.867  0.853
## [13091]  0.857  0.829  0.852  0.852  0.826  0.934  0.895  0.883  0.892  0.819
## [13101]  0.861  0.805  0.879  0.819  0.775  0.809  0.856  0.875  0.845  0.817
## [13111]  0.807  0.821  0.834  0.825  0.879  0.835  0.827  0.877  0.856  0.840
## [13121]  0.813  0.827  0.819  0.821  0.804  0.814  0.836  0.816  0.816  0.833
## [13131]  0.840  0.808  0.856  0.894  0.836  0.880  0.845  0.821  0.875  0.830
## [13141]  0.768  0.783  0.785  0.776  0.778  0.803  0.785  0.800  0.807  0.801
## [13151]  0.792  0.787  0.832  0.792  0.789  0.786  0.784  0.791  0.792  0.795
## [13161]  0.791  0.811  0.781  0.772  0.774  0.777  0.785  0.798  0.779  0.786
## [13171]  0.766  0.779  0.777  0.765  0.774  0.775  0.777  0.781  0.802  0.816
## [13181]  0.808  0.802  0.808  0.801  0.804  0.801  0.787  0.786  0.787  0.779
## [13191]  0.787  0.796  0.787  0.793  0.794  0.795  0.780  0.769  0.770  0.770
## [13201]  0.772  0.774  0.774  0.771  0.781  0.775  0.973  0.784  0.791  0.788
## [13211]  0.811  0.836  0.786  0.957  0.825  0.822  0.869  0.819  0.821  0.808
## [13221]  0.797  0.807  0.791  0.790  0.895  0.820  0.818  0.815  0.873  0.876
## [13231]  0.788  0.857  0.831  0.790  0.848  0.822  0.794  0.801  0.815  0.782
## [13241]  0.799  0.795  0.802  0.808  0.798  0.822  0.810  0.808  0.811  0.795
## [13251]  0.804  0.800  0.794  0.804  0.804  0.799  0.792  0.803  0.805  0.776
## [13261]  0.784  0.791  0.815  0.826  0.799  0.808  0.819  0.808  0.817  0.784
## [13271]  0.805  0.806  0.863  0.818  0.828  0.800  0.829  0.853  0.787  1.026
## [13281]  0.930  0.927  0.907  0.875  0.836  0.835  0.815  0.826  0.785  0.784
## [13291]  0.807  0.806  0.830  0.828  0.841  0.804  0.829  0.856  0.858  0.805
## [13301]  0.843  0.813  0.789  0.798  0.817  0.797  0.801  0.798  0.796  0.802
## [13311]  0.894  0.923  0.877  0.824  0.859  0.819  0.847  0.858  0.807  0.788
## [13321]  0.785  0.806  0.774  0.785  0.805  0.789  0.802  0.800  0.778  0.785
## [13331]  0.802  0.780  0.796  0.817  0.851  0.776  0.779  0.779  0.765  0.772
## [13341]  0.774  0.772  0.777  0.813  0.825  0.806  0.799  0.799  0.807  0.814
## [13351]  0.812  0.801  0.827  0.813  0.797  0.806  0.826  0.817  0.796  0.808
## [13361]  0.821  0.806  0.773  0.774  0.776  0.786  0.771  0.776  0.765  0.774
## [13371]  0.772  0.885  0.913  0.871  0.964  0.929  0.954  0.939  0.848  0.855
## [13381]  0.861  0.852  0.811  0.834  0.861  0.841  0.863  0.861  0.799  0.776
## [13391]  0.815  0.822  0.802  0.777  0.774  0.766  0.767  0.772  0.770  0.776
## [13401]  0.797  0.843  0.813  0.845  0.826  0.816  0.844  0.805  0.798  0.775
## [13411]  0.787  0.780  0.775  0.775  0.774  0.792  0.777  0.781  0.770  0.774
## [13421]  0.773  0.774  0.766  0.771  0.765  0.765  0.770  0.769  0.786  0.892
## [13431]  0.787  0.804  0.835  0.811  0.792  0.798  0.785  0.790  0.782  0.780
## [13441]  0.825  0.787  0.782  0.793  0.836  0.863  0.823  0.787  0.806  0.834
## [13451]  0.807  0.875  0.818  0.855  0.808  0.802  0.822  0.816  0.834  0.829
## [13461]  0.820  0.807  0.810  0.804  0.807  0.812  0.847  0.798  0.805  0.799
## [13471]  0.808  0.796  0.798  0.805  0.794  0.795  0.803  0.793  0.773  0.795
## [13481]  0.802  0.813  0.828  0.843  0.814  0.871  0.886  0.787  0.949  0.934
## [13491]  0.909  0.933  0.932  0.900  0.895  0.841  0.876  0.863  0.900  0.889
## [13501]  0.925  0.878  0.905  0.949  0.880  0.858  0.864  0.860  0.837  0.859
## [13511]  0.834  0.811  0.825  0.837  1.087  0.802  0.828  0.816  0.859  0.824
## [13521]  0.834  0.912  0.884  0.878  0.932  0.981  0.861  0.823  0.805  0.826
## [13531]  0.796  0.818  0.835  0.831  0.833  0.863  0.901  0.907  0.861  0.888
## [13541]  0.857  0.843  0.830  0.819  0.940  0.865  0.848  0.888  0.864  0.924
## [13551]  0.921  0.909  0.847  0.828  0.954  0.940  0.884  0.917  0.942  0.883
## [13561]  0.891  0.826  0.846  0.815  0.841  0.852  0.850  0.855  0.851  0.852
## [13571]  0.867  0.830  0.910  0.829  0.867  0.823  0.830  0.882  0.922  0.833
## [13581]  1.082  0.827  0.900  0.853  0.877  0.817  0.781  0.930  0.832  0.918
## [13591]  0.871  0.857  0.878  0.816  0.825  0.826  0.828  0.844  0.848  0.845
## [13601]  0.866  0.851  0.884  0.859  0.826  0.858  0.884  0.811  0.912  0.829
## [13611]  0.814  0.861  0.870  0.959  0.886  0.981  0.975  0.931  0.982  0.804
## [13621]  0.903  0.812  0.825  0.828  0.976  0.952  0.965  0.979  0.926  0.979
## [13631]  0.974  1.010  0.987  0.973  0.957  0.939  0.869  0.862  0.913  0.930
## [13641]  0.956  0.898  0.920  0.808  0.934  0.925  0.812  0.886  0.871  0.893
## [13651]  0.864  0.924  0.854  0.891  0.860  0.860  0.856  0.875  0.872  0.813
## [13661]  0.827  0.824  0.838  0.840  0.840  0.834  0.828  0.804  0.817  0.836
## [13671]  0.834  0.854  0.829  0.867  0.852  0.873  0.850  0.869  0.850  0.768
## [13681]  0.836  0.780  0.805  0.827  0.909  0.889  0.861  0.891  0.886  0.885
## [13691]  0.835  0.796  0.799  0.854  0.820  0.821  0.838  0.830  0.835  0.840
## [13701]  0.868  0.825  0.797  0.822  0.800  0.791  0.775  0.789  0.764  0.795
## [13711]  0.804  4.327  5.747  5.744  6.663  4.367  4.265  3.975  3.942  4.179
## [13721]  3.183  3.926  3.933  3.301  3.645  3.479  4.090  3.835  4.712  4.228
## [13731]  4.228  1.839  4.087  3.290  2.275  1.971  1.515  2.487  4.031  0.762
## [13741]  1.212  1.532  0.762  1.458  0.762  1.641  1.496  1.553  2.499  2.023
## [13751]  2.120  0.930  0.764  1.571  1.870  1.846  1.396  1.920  1.792  2.482
## [13761]  2.161  2.737  2.806  2.750  1.518  0.762  2.399  1.360  0.878  0.822
## [13771]  0.825  0.843  0.829  0.819  0.792  0.805  0.802  0.786  0.825  0.816
## [13781]  0.798  0.915  1.331  1.076  0.881  1.143  0.765  0.761  0.762  0.762
## [13791]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [13801]  0.762  0.762  0.762  0.762  0.762  0.762  0.761  0.761  0.761  0.761
## [13811]  0.761  0.761  0.761  0.761  0.761  0.763  0.763  0.760  0.815  0.999
## [13821]  0.781  0.768  0.761  1.421  1.175  1.180  1.527  0.761  0.761  1.648
## [13831]  0.973  1.190  0.762  1.545  0.762  1.973  1.066  2.005  0.844  2.066
## [13841]  0.762  2.205  0.762  1.783  0.762  1.833  0.762  1.058  0.762  0.761
## [13851]  0.841  1.646  1.387  1.808  1.365  1.840  1.656  2.108  1.221  1.788
## [13861]  1.312  1.525  0.868  0.970  0.895  0.760  0.824  0.834  0.873  1.196
## [13871]  0.867  1.157  0.989  1.950  1.113  2.569  2.296  1.503  1.123  1.458
## [13881]  0.761  1.243  1.243  1.233  1.767  1.318  1.730  1.434  1.390  1.504
## [13891]  1.269  1.705  1.352  1.506  1.799  1.729  1.531  1.673  1.266  1.027
## [13901]  0.947  0.840  1.216  1.147  1.005  1.124  1.075  1.066  1.030  0.779
## [13911]  0.777  0.781  0.779  0.790  0.793  0.785  0.775  0.772  0.779  0.765
## [13921]  0.767  0.780  0.773  0.766  0.976  0.761  0.761  0.761  0.761  0.761
## [13931]  0.762  0.761  0.762  0.762  0.762  0.760  0.761  0.966  0.760  0.766
## [13941]  0.762  0.808  0.760  0.792  0.760  0.762  0.760  0.767  0.760  0.764
## [13951]  0.760  0.760  0.760  0.761  0.761  0.762  0.761  0.761  0.761  0.761
## [13961]  0.761  0.761  0.761  0.761  0.761  0.761  0.941  0.773  0.776  0.761
## [13971]  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.762  0.762  0.762
## [13981]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [13991]  0.761  0.761  0.762  0.761  0.761  0.822  0.762  0.762  0.761  0.761
## [14001]  0.761  0.761  0.761  1.366  0.762  0.762  0.761  0.761  0.761  0.761
## [14011]  0.761  0.761  1.595  0.875  0.761  0.761  0.766  1.541  0.762  0.762
## [14021]  0.762  0.762  0.762  0.762  3.043  1.229  1.741  0.859  1.955  0.762
## [14031]  0.762  0.762  0.761  0.761  0.762  0.761  0.761  0.762  0.762  0.762
## [14041]  0.761  0.761  0.761  0.761  0.761  0.762  0.762  0.769  0.761  0.761
## [14051]  0.761  0.761  0.798  0.811  0.761  0.761  0.839  0.812  0.762  0.798
## [14061]  0.762  0.800  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [14071]  0.762  0.762  0.761  0.761  0.762  0.761  0.761  0.762  0.762  0.762
## [14081]  0.761  0.761  0.761  0.761  0.761  1.519  1.043  0.837  0.761  0.761
## [14091]  0.880  0.761  1.541  2.276  1.464  1.816  1.739  1.571  1.983  1.227
## [14101]  0.762  1.940  0.762  1.594  1.843  1.756  0.971  0.909  0.774  0.761
## [14111]  0.769  0.761  0.777  0.761  0.776  0.761  0.772  0.761  0.769  0.762
## [14121]  0.773  0.777  0.797  0.781  0.761  0.787  0.761  0.813  0.761  0.782
## [14131]  0.764  0.792  0.970  0.761  0.761  0.761  0.761  0.761  0.761  0.760
## [14141]  0.761  0.762  0.816  0.762  0.762  0.762  0.762  0.761  0.762  0.761
## [14151]  0.761  0.762  0.761  0.761  0.761  0.920  0.761  0.762  0.775  0.761
## [14161]  0.789  0.921  0.943  0.957  0.966  0.944  0.829  0.939  0.902  0.952
## [14171]  0.900  0.943  0.953  0.949  0.908  0.908  0.761  0.761  0.761  0.761
## [14181]  0.761  0.762  0.761  0.762  0.762  0.762  0.761  0.957  0.941  0.946
## [14191]  0.875  0.942  1.034  1.953  2.180  1.072  1.245  0.847  3.274  1.189
## [14201]  1.149  1.319  0.778  0.799  0.782  0.779  0.784  0.774  0.790  0.761
## [14211]  0.784  0.761  0.800  1.221  0.794  0.761  0.862  0.868  0.845  0.847
## [14221]  0.811  0.957  0.915  0.773  0.924  0.761  0.839  0.835  0.761  0.761
## [14231]  0.799  0.818  0.813  0.805  0.827  0.784  0.799  0.803  0.842  0.790
## [14241]  0.782  0.789  0.761  0.762  0.771  0.789  0.776  1.265  0.777  0.934
## [14251]  0.783  0.761  0.763  0.761  0.771  0.761  0.806  2.332  0.797  1.479
## [14261]  0.812  1.464  0.771  0.878  0.806  0.767  0.768  0.765  0.784  1.496
## [14271]  0.763  0.763  0.762  1.079  0.762  0.761  0.762  0.761  0.761  0.761
## [14281]  0.805  0.764  1.417  1.202  1.564  1.549  1.688  2.058  0.761  0.762
## [14291]  0.763  0.762  0.762  0.762  0.762  0.765  0.762  0.761  0.761  0.762
## [14301]  0.761  0.761  0.762  0.761  0.762  0.761  0.777  0.787  0.795  0.801
## [14311]  0.787  0.800  0.828  0.816  0.803  0.808  0.820  0.790  0.802  0.813
## [14321]  0.840  0.865  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.762
## [14331]  0.762  0.762  0.761  0.855  0.798  0.799  0.815  0.786  1.067  1.162
## [14341]  1.121  1.138  1.135  1.131  1.160  1.186  1.122  1.088  1.140  1.127
## [14351]  1.079  0.762  1.129  0.762  0.761  0.870  0.761  0.760  0.761  0.761
## [14361]  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761  0.762  0.761
## [14371]  0.761  0.761  0.764  0.761  0.762  1.511  0.761  0.762  0.762  0.762
## [14381]  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.761
## [14391]  0.761  0.762  1.065  0.761  1.072  1.123  0.761  1.276  1.438  0.760
## [14401]  0.761  0.762  0.763  0.762  0.762  0.762  0.762  0.761  0.762  0.761
## [14411]  0.761  0.762  0.761  0.761  0.761  0.761  0.762  0.761  1.002  0.762
## [14421]  0.761  0.762  0.762  0.762  0.762  4.353  8.238  2.898  5.960  1.663
## [14431]  1.168  0.770  0.983  0.993  0.875  0.780  0.764  0.772  0.774  0.772
## [14441]  0.763  0.764  0.764  0.762  0.762  0.762  0.762  0.763  0.764  0.789
## [14451]  0.765  0.942  0.762  0.801  0.769  0.781  0.819  0.770  0.762  0.762
## [14461]  0.762  3.831  7.223  2.179  2.172  2.733  1.778  3.007  2.739  3.861
## [14471]  4.323  5.778  4.822  2.890  2.680  2.051  2.812  2.180  5.758  2.142
## [14481]  3.706  6.758  4.337 11.107  1.856  1.721  1.361  3.869  1.371  1.216
## [14491]  0.817  0.762  0.761  0.763  5.272  6.878  1.523  1.679  0.877  3.506
## [14501]  2.368  1.662  4.287  2.632  0.958  0.901  1.771  3.610  0.761  4.940
## [14511]  0.952  0.761  0.761  0.761  0.761  1.435  0.761  1.509  0.948  0.762
## [14521]  0.762  0.762  0.762  2.136 10.288  5.504  0.761  1.264  1.010  0.761
## [14531]  0.761  0.761  0.761  3.825  2.962  0.761  8.253  2.152  2.014  3.858
## [14541]  3.813  2.646  6.501  3.706  1.958  1.662  1.875  0.762  1.672  4.986
## [14551]  8.326  6.771  3.151  3.925  1.135  1.360  1.240  1.156  0.761  6.202
## [14561]  6.093  3.201  5.867  2.927  3.934  3.625  4.717  2.414  5.743  2.253
## [14571]  3.027  4.868  1.672  2.686  1.291  5.197  3.668  3.049  2.354  3.948
## [14581]  4.113  4.665  5.629  5.294  2.669  5.755  1.286  3.243  1.808  2.908
## [14591]  6.710  8.595  6.606  3.964  4.516  3.395  3.610  3.104  1.914  2.905
## [14601]  1.512  2.685  0.819  1.182  1.958  2.745  1.886  0.872  3.156  3.407
## [14611]  5.436  5.951  3.141  4.263  4.465  3.914  3.212  2.939  1.540  0.762
## [14621]  0.762  0.761  0.761  0.761  0.761  0.761  0.762  1.091  0.769  0.958
## [14631]  0.811  1.025  1.039  0.761  1.141  0.775  0.761  0.925  0.761  0.761
## [14641]  0.761  0.869  0.761  1.062  0.891  0.796  0.969  0.778  1.017  0.875
## [14651]  1.441  1.594  1.546  1.516  1.514  0.760  0.820  1.916  0.761  0.761
## [14661]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [14671]  0.761  1.164  2.279  0.762  2.051  0.762  0.762  0.762  0.762  0.762
## [14681]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.761  0.916  2.236
## [14691]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.761  0.761  0.761
## [14701]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [14711]  0.761  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [14721]  0.762  0.762  0.762  0.762  0.765  0.761  0.761  0.826  0.762  0.762
## [14731]  0.762  0.762  0.762  0.762  0.762  0.815  0.762  0.761  0.761  0.761
## [14741]  1.111  1.330  1.411  0.827  0.761  0.771  0.761  1.054  0.762  0.982
## [14751]  0.762  0.761  0.800  0.761  0.807  0.863  0.811  0.761  0.803  0.761
## [14761]  0.811  0.761  0.810  0.797  0.794  0.807  0.762  0.762  0.762  0.762
## [14771]  0.762  0.762  0.762  0.762  0.762  0.761  0.761  0.761  0.932  0.761
## [14781]  1.147  0.761  1.116  0.956  1.156  0.761  1.075  1.332  1.134  0.936
## [14791]  1.081  1.130  1.128  1.132  1.020  1.110  1.157  1.123  1.119  1.143
## [14801]  0.866  1.109  1.018  1.404  0.896  1.191  0.772  1.136  0.761  1.106
## [14811]  0.761  1.119  0.761  1.143  0.815  1.003  0.797  0.993  0.773  1.077
## [14821]  0.800  1.006  0.776  1.820  0.959  1.892  1.329  4.095  2.290  1.982
## [14831]  4.089  1.011  0.851  0.761  0.761  1.033  0.761  1.124  0.761  1.095
## [14841]  1.045  1.108  1.031  1.046  1.073  0.764  1.051  1.109  1.115  1.023
## [14851]  0.761  1.157  0.901  1.172  0.862  1.123  0.761  1.087  0.761  0.761
## [14861]  0.761  0.761  0.761  0.761  0.770  1.067  1.145  1.154  1.141  1.069
## [14871]  1.094  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [14881]  0.762  0.761  0.762  2.096  1.496  2.075  1.185  0.762  0.761  0.761
## [14891]  0.761  0.761  0.761  0.761  0.848  0.761  0.761  0.761  0.761  1.030
## [14901]  3.820  3.376  2.338  1.185  0.761  0.761  0.761  0.761  0.761  0.761
## [14911]  0.761  0.761  0.761  0.761  0.761  0.761  1.022  2.371  3.153  2.001
## [14921]  3.050  2.861  1.892  0.997  0.781  1.035  1.517  0.760  0.762  0.761
## [14931]  0.761  0.762  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.762
## [14941]  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.762
## [14951]  0.761  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.761  0.761
## [14961]  0.761  0.762  0.761  0.762  0.760  0.760  0.761  0.760  0.762  0.761
## [14971]  0.760  0.762  0.761  0.760  0.761  0.761  0.760  0.762  0.760  0.761
## [14981]  0.762  0.760  0.761  0.760  0.761  0.761  0.760  0.761  0.762  0.760
## [14991]  0.761  1.133  0.760  0.760  0.761  0.761  0.761  0.761  0.761  0.760
## [15001]  0.761  0.761  0.761  0.761  0.760  0.760  0.760  0.761  0.761  0.761
## [15011]  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.763
## [15021]  0.761  0.761  0.761  0.761  0.760  0.763  0.761  0.761  0.763  0.762
## [15031]  0.761  0.763  0.761  0.762  0.766  0.761  0.766  0.761  0.766  0.761
## [15041]  1.832  0.761  1.336  0.761  0.761  0.761  0.766  0.761  0.761  0.761
## [15051]  0.762  0.761  0.762  0.762  0.762  0.761  0.761  0.760  2.000  0.761
## [15061]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [15071]  0.761  1.313  0.824  0.760  0.844  0.761  0.761  2.546  0.762  3.286
## [15081]  0.762  0.761  0.761  0.762  0.761  0.762  0.761  0.761  0.761  0.761
## [15091]  0.760  0.761  1.375  0.760  0.762  1.375  0.760  0.761  0.761  0.760
## [15101]  0.761  0.763  0.760  0.760  0.761  0.761  1.083  0.761  0.761  0.761
## [15111]  0.761  0.760  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.762
## [15121]  0.761  0.761  0.762  0.763  0.761  0.761  0.761  0.766  0.762  2.777
## [15131]  1.312  3.636  0.938  0.911  0.913  0.945  0.922  0.976  0.906  0.822
## [15141]  0.787  0.867  0.871  0.980  0.912  0.907  0.864  0.918  0.912  0.887
## [15151]  0.969  0.760  0.760  0.760  0.760  0.760  0.760  0.760  0.760  1.491
## [15161]  0.840  1.006  0.811  0.972  0.926  0.870  0.761  0.903  0.761  0.867
## [15171]  0.762  0.928  0.761  0.925  0.760  0.911  0.762  0.905  0.762  0.940
## [15181]  0.762  0.894  0.761  0.951  0.761  0.951  0.761  0.908  0.761  0.970
## [15191]  0.761  0.906  0.761  0.860  0.761  0.909  0.761  0.933  0.761  0.928
## [15201]  0.766  0.974  0.998  0.845  0.938  0.967  0.952  0.968  1.522  2.140
## [15211]  1.287  1.938  0.762  0.761  0.761  0.762  0.761  0.760  0.762  0.761
## [15221]  0.761  0.761  0.761  0.952  0.925  0.967  0.943  0.927  0.856  0.867
## [15231]  0.953  0.884  0.761  0.923  0.888  0.862  0.827  0.935  0.857  0.916
## [15241]  0.889  0.925  0.925  0.915  0.952  0.880  0.917  0.815  0.882  0.916
## [15251]  0.772  0.835  0.931  0.845  0.807  0.926  0.949  0.801  0.783  0.926
## [15261]  0.938  0.945  0.777  0.879  0.931  0.777  0.761  0.905  0.763  0.762
## [15271]  0.763  0.846  0.761  0.761  0.925  0.809  0.835  0.761  0.925  0.760
## [15281]  0.760  0.784  0.763  0.760  0.762  0.761  0.763  0.760  0.762  0.761
## [15291]  0.763  0.760  0.764  0.774  0.763  0.760  0.761  0.800  0.782  0.760
## [15301]  0.876  0.770  0.797  0.760  1.809  0.763  0.760  0.837  0.761  0.760
## [15311]  0.761  0.767  1.237  0.773  0.761  0.760  0.761  0.841  1.284  0.780
## [15321]  1.307  0.829  1.373  0.782  0.798  0.798  0.761  0.805  0.760  0.761
## [15331]  0.895  0.833  0.761  1.150  0.765  0.761  0.761  0.773  0.935  0.761
## [15341]  0.762  0.761  0.807  0.834  0.797  0.761  0.762  0.832  0.762  0.762
## [15351]  0.762  0.761  0.839  0.761  0.780  0.762  0.760  0.867  0.772  0.764
## [15361]  0.762  0.762  0.896  0.770  0.807  0.762  0.762  0.788  0.762  0.761
## [15371]  0.762  0.762  0.807  0.761  0.828  0.761  0.818  0.763  0.878  0.761
## [15381]  0.804  0.762  0.910  0.761  0.879  0.761  0.933  0.761  0.820  0.761
## [15391]  0.922  0.761  0.768  0.782  0.846  0.761  0.852  0.811  0.958  0.761
## [15401]  0.928  0.761  0.971  0.761  0.825  0.781  0.921  0.761  0.870  0.761
## [15411]  0.893  0.766  0.824  0.780  0.935  0.924  0.913  0.856  0.859  0.940
## [15421]  0.933  0.846  0.884  0.950  0.873  0.791  0.926  0.802  0.761  0.764
## [15431]  0.762  0.762  0.782  0.761  1.376  0.782  0.792  0.794  0.865  0.918
## [15441]  0.844  0.760  0.760  0.937  0.760  0.931  0.940  0.760  0.954  0.866
## [15451]  0.760  0.800  0.866  0.866  1.095  1.525  0.831  0.778  0.760  1.220
## [15461]  0.762  1.009  0.798  0.762  0.846  0.889  0.762  0.830  0.908  0.761
## [15471]  0.761  1.380  0.814  0.761  0.829  0.761  2.053  0.864  0.761  1.342
## [15481]  0.873  0.761  1.474  0.800  0.761  1.411  0.815  0.761  1.314  0.761
## [15491]  0.766  0.940  0.867  0.906  0.927  0.898  0.914  0.930  0.902  0.920
## [15501]  0.929  0.883  0.868  0.918  0.889  0.926  0.894  0.942  0.942  0.760
## [15511]  0.760  0.958  0.762  0.761  0.761  0.761  0.766  0.932  0.903  0.919
## [15521]  2.797  4.270  6.197  3.766  3.937  3.400  2.105  2.117  1.982  1.912
## [15531]  1.826  1.208  1.951  2.383  1.239  2.418  1.520  2.177  1.982  2.304
## [15541]  1.230  1.271  1.335  1.292  1.886  0.762  3.224  2.928  3.258  3.273
## [15551]  2.824  1.145  0.915  1.060  1.218  2.260  2.377  0.761  4.008  0.761
## [15561]  7.483  0.761 10.194  0.761  7.092  1.507  8.171  1.199  7.961  2.512
## [15571]  8.048  0.761  6.367  0.761  5.661  5.727  2.974  3.568  3.919  6.383
## [15581]  2.240  0.761  0.761  0.761  0.761  0.761  0.761  0.762  1.152  1.060
## [15591]  1.068  1.051  1.082  1.097  1.029  1.085  1.006  1.007  1.018  0.926
## [15601]  0.938  1.821  1.018  0.968  1.815  1.786  1.668  2.364  1.704  0.761
## [15611]  0.761  0.761  0.761  0.761  0.761  0.761  1.224  1.168  1.219  0.896
## [15621]  1.254  1.276  1.243  2.690  0.761  1.191  2.251  1.835  1.288  3.220
## [15631]  1.284  1.211  1.327  1.020  2.057  1.563  1.211  0.969  2.515  2.494
## [15641]  1.193  0.888  6.026  1.728  0.911  1.011  5.446  2.549  1.036  0.983
## [15651]  1.534  2.477  0.908  0.888  2.150  0.875  1.335  1.298  1.834  0.925
## [15661]  0.891  1.226  2.661  1.018  0.950  1.680  2.407  1.349  0.957  1.235
## [15671]  1.896  0.867  1.050  1.142  1.633  0.997  0.963  2.424  1.617  0.937
## [15681]  0.869  1.941  1.088  0.881  0.871  1.052  1.605  0.999  1.176  1.236
## [15691]  1.666  1.001  0.858  1.499  1.906  0.851  0.875  1.193  1.594  1.867
## [15701]  0.875  2.387  1.078  0.924  0.874  2.354  1.032  0.843  0.858  1.926
## [15711]  1.834  0.961  0.866  2.310  1.862  0.950  0.870  1.859  1.793  1.590
## [15721]  1.915  3.120  2.501  2.035  1.899  2.789  1.369  2.249  2.491  3.089
## [15731]  0.762  1.475  0.766  3.157  1.990  1.840  1.938  2.064  1.344  1.940
## [15741]  1.523  1.108  3.729  1.977  0.982  2.219  1.608  1.497  0.995  0.761
## [15751]  1.883  0.933  1.292  0.898  1.047  1.640  3.160  1.309  1.058  0.761
## [15761]  2.179  1.907  1.856  3.524  1.191  0.965  1.539  5.260  2.735  0.966
## [15771]  1.481  5.987  2.045  1.519  1.528  5.331  2.187  0.911  1.430  6.991
## [15781]  1.517  0.847  0.936  5.009  2.530  1.099  0.940  4.542  1.530  0.761
## [15791]  0.947  2.980  2.813  0.865  0.951  1.615  2.493  0.827  0.917  1.528
## [15801]  1.641  1.172  0.966  1.505  2.162  0.820  0.879  1.534  1.283  0.764
## [15811]  1.190  1.530  1.904  1.062  0.953  0.873  1.343  1.406  0.761  1.072
## [15821]  0.761  0.941  2.087  1.056  0.761  0.982  0.761  1.026  1.182  1.019
## [15831]  0.761  1.002  0.761  1.145  0.761  1.034  0.761  0.912  0.761  1.036
## [15841]  1.238  1.575  0.761  0.939  1.849  0.906  2.477  1.530  1.195  1.054
## [15851]  0.762  1.039  1.228  1.072  1.232  1.337  1.316  1.380  2.840  2.536
## [15861]  2.294  3.693  3.082  3.394  2.465  2.180  3.043  3.703  3.192  3.673
## [15871]  3.130  3.101  3.422  2.900  3.472  3.299  2.863  3.122  2.281  2.662
## [15881]  2.926  2.280  1.575  0.761  1.909  1.971  3.102  2.304  1.248  6.410
## [15891]  1.634  1.001  2.913  2.238  1.072  3.631  1.277  0.973  0.761  1.432
## [15901]  0.979  3.439  0.990  0.950  3.186  1.295  0.872  3.703  1.278  0.761
## [15911]  4.658  1.102  0.991  5.929  1.139  0.973  3.439  1.286  2.347  1.011
## [15921]  0.994  0.846  1.498  1.030  1.241  1.128  2.731  0.764  1.431  4.574
## [15931]  1.742  1.970  1.520  2.832  1.721  5.992  1.477  1.496  0.761  1.496
## [15941]  1.522  1.496  0.762  0.761  1.618  0.761  0.761  1.218  0.761  1.623
## [15951]  1.060  2.267  1.456  1.564  1.242  1.251  0.996  1.655  1.977  3.533
## [15961]  2.102  2.501  2.856  2.883  2.716  2.779  2.438  2.111  2.869  2.911
## [15971]  1.587  1.412  2.269  1.398  1.484  3.349  3.366  2.444  0.761  1.902
## [15981]  1.481  1.525  1.341  2.239  2.001  0.761  1.516  1.496  0.761  0.761
## [15991]  0.761  0.845  0.987  0.761  0.896  1.007  0.761  0.761  1.676  0.761
## [16001]  0.892  1.010  0.761  0.908  1.029  0.762  0.761  1.329  1.181  0.761
## [16011]  1.070  1.029  0.761  0.992  0.791  0.761  1.073  0.999  0.761  0.978
## [16021]  1.042  0.761  2.994  1.082  0.761  1.862  1.012  0.763  2.735  0.835
## [16031]  0.761  2.434  1.173  0.761  2.538  0.820  0.761  0.975  0.851  0.761
## [16041]  3.063  0.761  0.761  0.822  0.805  0.761  2.024  0.761  0.798  0.761
## [16051]  2.292  0.831  0.761  0.761  2.982  0.815  0.762  0.922  3.153  0.761
## [16061]  0.961  0.761  1.118  3.020  0.848  1.449  0.948  1.380  0.761  1.163
## [16071]  6.871  1.687  1.574  0.851  1.401  1.626  1.278  3.692  1.785  3.801
## [16081]  2.259  0.877  1.919  1.044  0.904  2.386  2.142  1.247  1.181  3.859
## [16091]  0.962  0.997  0.805  0.761  0.944  0.802  0.761  1.036  0.810  0.761
## [16101]  1.161  0.826  0.761  1.184  0.908  0.761  1.085  1.182  0.761  1.036
## [16111]  1.675  0.761  1.058  0.882  0.940  0.765  0.932  0.761  0.790  0.822
## [16121]  0.761  0.870  0.761  0.911  0.833  0.815  0.888  0.849  0.809  0.995
## [16131]  0.778  0.868  0.761  0.761  1.142  0.761  0.988  1.110  0.761  0.761
## [16141]  0.983  0.761  0.761  0.817  0.762  0.762  0.876  0.793  0.775  0.801
## [16151]  0.891  0.887  0.777  0.801  0.848  0.803  0.805  0.765  0.768  0.769
## [16161]  0.770  0.769  0.773  0.768  0.777  0.804  0.842  0.839  0.776  0.764
## [16171]  0.763  0.767  0.768  0.769  0.769  0.768  0.772  0.766  0.766  0.765
## [16181]  0.764  0.764  0.764  0.765  0.776  0.767  0.774  0.763  0.765  0.765
## [16191]  0.763  0.777  0.765  0.765  0.766  0.764  0.765  0.765  0.786  0.762
## [16201]  0.764  0.763  0.763  0.767  0.763  0.763  0.763  0.763  0.764  0.763
## [16211]  0.763  0.768  0.764  0.763  0.762  0.762  0.771  0.783  0.809  0.854
## [16221]  0.881  0.895  0.899  0.930  0.807  0.805  0.773  0.775  0.812  0.805
## [16231]  0.795  0.790  0.833  0.819  0.807  0.761  0.761  0.761  0.761  0.761
## [16241]  0.793  0.790  0.766  0.766  0.776  0.824  0.779  0.771  0.768  0.762
## [16251]  0.762  0.764  0.777  0.773  0.772  0.766  0.769  0.765  0.770  0.765
## [16261]  0.774  0.774  0.764  0.763  0.790  0.794  0.761  0.774  0.990  0.847
## [16271]  1.051  1.028  0.955  1.037  1.011  1.001  1.111  1.429  1.280  1.402
## [16281]  1.174  1.101  1.371  1.426  1.279  1.655  1.179  1.439  3.014  0.771
## [16291]  0.763  0.765  0.767  0.765  0.768  0.794  0.833  0.826  0.881  0.869
## [16301]  0.846  0.831  0.869  0.784  0.783  0.789  0.786  0.779  0.800  0.794
## [16311]  0.823  0.855  0.761  0.761  0.761  0.761  0.763  0.845  0.904  0.927
## [16321]  0.763  0.762  0.762  0.822  0.823  0.783  0.868  0.858  0.761  0.844
## [16331]  0.790  0.955  0.777  0.894  0.807  0.890  0.862  1.079  0.761  0.761
## [16341]  0.761  0.800  0.762  0.761  0.762  0.764  0.765  0.762  0.762  0.818
## [16351]  0.882  0.914  0.892  0.888  0.900  0.793  0.786  0.768  0.767  0.771
## [16361]  0.762  0.770  0.766  0.770  0.765  0.766  0.770  0.765  0.765  0.900
## [16371]  0.881  0.761  0.761  0.851  0.818  0.849  0.782  0.813  0.854  0.854
## [16381]  0.853  0.882  0.808  0.778  0.763  0.761  0.762  0.820  0.826  0.870
## [16391]  0.871  0.926  0.919  0.928  0.891  0.772  0.816  0.761  0.761  0.796
## [16401]  0.790  0.825  0.834  0.831  0.871  0.821  0.827  0.852  0.986  0.938
## [16411]  0.795  0.888  0.833  0.831  0.867  1.120  0.820  0.881  0.866  0.845
## [16421]  0.841  0.832  0.845  0.858  0.859  0.819  0.842  0.838  0.826  0.852
## [16431]  0.846  0.841  0.855  0.883  0.848  0.906  0.880  0.871  0.876  0.895
## [16441]  0.847  0.919  1.017  0.829  0.814  0.897  0.842  0.774  0.814  0.803
## [16451]  0.839  0.922  0.913  0.951  0.977  0.943  0.948  0.859  1.063  1.027
## [16461]  1.114  0.833  0.905  0.948  1.006  0.826  1.003  1.032  0.975  1.060
## [16471]  0.944  1.019  0.921  0.937  0.902  0.906  0.912  0.953  0.917  0.936
## [16481]  0.957  0.936  0.925  0.944  0.762  0.857  0.916  0.761  0.891  0.911
## [16491]  0.843  0.923  1.000  0.902  0.999  1.065  1.094  0.969  1.064  0.910
## [16501]  0.928  1.018  0.912  1.017  1.060  0.904  1.042  0.999  1.002  1.060
## [16511]  1.124  0.942  1.016  1.199  0.927  1.086  1.054  1.073  1.121  1.085
## [16521]  0.968  0.945  1.076  0.977  1.050  1.036  0.988  1.078  1.086  1.048
## [16531]  1.175  1.064  1.055  1.092  1.326  1.057  1.060  1.171  1.306  1.065
## [16541]  1.251  1.079  1.005  1.327  1.067  1.021  0.971  1.089  1.123  0.925
## [16551]  0.931  0.775  0.964  1.172  1.002  1.005  1.172  1.167  1.518  1.441
## [16561]  1.106  1.079  1.041  1.085  1.105  1.012  1.008  1.024  1.009  1.007
## [16571]  1.074  1.010  1.083  1.040  1.003  1.093  0.761  0.761  0.761  0.761
## [16581]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [16591]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [16601]  0.762  0.863  0.761  0.868  0.761  0.760  0.761  0.761  0.760  0.761
## [16611]  0.831  0.840  0.761  0.825  0.761  0.854  0.761  0.860  0.761  0.847
## [16621]  0.761  0.831  0.761  0.828  0.761  0.838  0.761  0.838  0.761  1.510
## [16631]  0.761  1.299  0.761  1.536  0.761  1.808  0.761  1.460  1.552  1.554
## [16641]  0.761  1.537  1.556  1.563  1.566  1.592  1.610  0.761  1.536  1.600
## [16651]  1.575  1.506  1.537  1.556  1.585  1.554  1.574  1.600  1.556  1.568
## [16661]  1.206  0.994  0.761  1.010  0.761  0.899  0.872  0.880  0.811  0.883
## [16671]  0.889  0.761  0.868  1.035  0.831  0.901  0.854  0.796  0.834  0.761
## [16681]  0.855  0.761  0.885  0.761  0.839  1.007  0.838  0.946  0.844  0.761
## [16691]  0.818  0.778  0.801  0.885  0.972  0.944  0.951  0.972  0.975  0.951
## [16701]  0.899  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [16711]  0.761  0.761  0.761  0.763  0.761  0.761  0.761  0.761  0.761  0.761
## [16721]  0.761  0.761  0.761  0.761  0.770  0.766  0.761  0.761  0.761  0.761
## [16731]  0.761  0.761  0.761  0.762  0.761  0.762  0.761  0.762  0.761  0.762
## [16741]  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.762  0.761  0.762
## [16751]  0.761  0.762  0.761  0.761  0.761  0.761  0.763  0.762  0.761  0.761
## [16761]  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762
## [16771]  0.761  0.762  0.761  0.761  0.761  0.761  0.762  0.762  0.762  0.762
## [16781]  0.762  0.762  0.762  0.762  0.762  0.761  0.762  0.762  0.761  0.761
## [16791]  0.761  0.761  0.761  0.824  0.761  0.781  0.762  0.762  0.761  0.762
## [16801]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.762
## [16811]  0.761  0.920  0.843  0.867  0.783  0.915  0.799  0.782  0.811  0.860
## [16821]  0.842  0.915  0.837  0.809  0.843  0.855  0.791  0.967  0.906  0.867
## [16831]  1.809  0.907  1.584  0.806  1.673  0.788  1.747  0.867  1.761  0.868
## [16841]  1.754  0.921  1.838  0.941  1.845  0.936  1.794  0.953  1.721  0.975
## [16851]  1.713  1.041  1.967  0.898  1.914  0.933  2.005  0.871  1.635  0.991
## [16861]  1.265  1.902  1.628  1.606  1.462  1.206  1.714  1.901  1.623  1.638
## [16871]  1.514  0.981  0.907  0.860  0.986  0.842  0.888  0.786  0.845  0.785
## [16881]  0.910  0.784  0.951  0.831  0.842  0.861  0.921  0.880  1.084  0.911
## [16891]  0.903  0.896  0.909  0.771  0.954  0.816  0.985  0.830  0.903  0.800
## [16901]  0.899  0.761  0.761  0.861  0.865  0.761  0.761  1.922  1.811  1.437
## [16911]  1.942  1.713  2.046  2.167  1.646  2.132  1.539  2.442  2.140  2.281
## [16921]  2.184  1.103  2.361  2.280  2.179  2.039  1.840  2.814  2.307  2.654
## [16931]  1.861  3.452  2.322  3.316  2.201  3.653  2.260  3.723  2.778  4.404
## [16941]  2.534  5.732  0.907  2.261  2.365  2.593  2.678  2.650  3.306  1.907
## [16951]  4.226  1.762  3.440  2.742  3.663  6.081  2.802  4.764  1.744  1.959
## [16961]  3.817  3.287  5.968  5.266  2.224  2.787  4.480  7.055  5.270  5.713
## [16971]  5.303  3.681  4.717  4.598  2.848  2.966  3.109  3.156  2.149  2.399
## [16981]  2.170  2.131  1.948  1.754  1.414  2.439  1.116  1.668  1.551  1.701
## [16991]  0.800  1.809  2.239  1.639  2.078  2.055  1.990  2.532  2.691  1.126
## [17001]  2.643  2.512  2.457  3.262  2.176  2.457  2.831  1.820  1.738  1.540
## [17011]  1.628  1.777  1.792  1.533  0.762  0.762  0.762  0.762  0.762  0.762
## [17021]  0.762  0.762  0.762  0.813  1.084  1.168  1.588  1.493  1.335  1.374
## [17031]  1.344  1.587  1.212  1.523  0.761  0.762  1.528  0.761  1.294  1.989
## [17041]  2.813  1.344  1.628  1.572  1.484  2.521  5.745  5.690  2.718  5.975
## [17051]  8.509  7.192  5.954  1.774  2.408  1.490  1.702  1.012  1.149  1.263
## [17061]  0.942  1.660  1.884  2.303  1.694  1.190  2.081  2.249  2.181  2.177
## [17071]  2.069  2.241  1.970  1.962  2.166  1.979  2.315  2.551  1.015  2.226
## [17081]  2.924  3.231  3.726  2.565  4.009  3.730  1.693  2.471  1.030  2.132
## [17091]  2.102  2.178  1.635  0.924  1.605  1.684  1.823  1.601  2.066  1.809
## [17101]  1.593  2.401  1.373  2.106  2.309  2.154  2.369  1.878  2.413  2.318
## [17111]  2.137  1.941  2.042  3.060  2.166  3.072  2.008  3.622  2.164  3.535
## [17121]  2.312  3.368  2.348  3.537  2.827  4.070  2.231  2.893  1.108  1.854
## [17131]  2.252  3.087  2.584  3.095  2.072  4.020  4.407  4.442  4.619  3.443
## [17141]  7.920  9.410  1.980  2.305  3.841  2.924  3.970  3.106  6.181  5.258
## [17151]  1.706  3.770  8.613  3.881  2.934  2.011  3.122  1.884  2.828  4.406
## [17161]  1.542  2.369  2.604  2.510  1.738  1.720  2.178  1.798  1.648  1.723
## [17171]  2.037  1.300  1.631  1.519  1.666  2.058  2.074  2.160  1.704  2.075
## [17181]  1.978  2.756  2.973  3.319  3.690  3.336  3.755  2.869  2.280  1.587
## [17191]  3.990  5.722  3.290  5.035  3.311  5.308  2.129  2.087  0.891  1.002
## [17201]  0.902  1.227  1.082  1.313  1.097  1.763  2.243  1.564  0.973  1.658
## [17211]  0.826  1.983  0.842  1.984  0.826  2.127  1.273  1.710  1.633  1.804
## [17221]  2.633  2.665  0.762  0.761  1.393  0.761  1.243  1.392  2.179  2.992
## [17231]  3.018  2.211  1.843  5.192  1.953  3.112  1.884  2.699  1.334  1.273
## [17241]  9.000  8.350  2.638  3.236  2.681  4.854  3.304  1.740  3.152  2.870
## [17251]  1.371  1.574  1.450  0.999  1.465  1.634  1.351  1.290  1.263  1.020
## [17261]  1.028  2.058  7.328  0.762  0.762  0.762  0.762  0.761  0.762  0.762
## [17271]  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761
## [17281]  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762
## [17291]  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762
## [17301]  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762
## [17311]  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762
## [17321]  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762
## [17331]  0.761  0.762  0.762  0.762  1.358  0.762  0.762  0.762  0.761  0.762
## [17341]  0.762  0.762  1.540  0.762  0.762  0.762  1.228  0.762  0.762  0.762
## [17351]  1.345  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762
## [17361]  0.762  0.762  1.361  0.762  0.762  0.762  0.761  0.762  0.762  0.762
## [17371]  1.501  0.762  0.762  1.110  2.193  0.762  0.762  1.397  1.163  0.761
## [17381]  0.762  2.184  1.383  0.762  0.762  2.098  2.215  0.761  1.035  0.762
## [17391]  1.308  0.762  1.438  2.111  1.716  1.639  0.761  1.471  1.485  0.761
## [17401]  1.376  1.920  0.762  0.761  0.762  0.762  1.481  0.762  1.520  0.762
## [17411]  0.762  2.273  0.762  0.762  0.762  0.762  1.292  0.762  1.439  0.762
## [17421]  1.473  1.153  0.762  0.762  1.202  0.858  0.762  0.762  1.095  0.762
## [17431]  0.762  0.762  1.462  0.762  0.762  0.762  1.390  0.762  0.762  0.762
## [17441]  1.525  0.762  0.762  0.762  1.941  0.762  0.762  0.762  0.916  0.762
## [17451]  0.762  0.762  1.017  0.762  0.762  0.762  1.453  0.762  1.471  0.762
## [17461]  0.761  0.762  1.445  1.458  1.230  0.762  1.278  0.762  0.761  0.762
## [17471]  0.762  0.762  0.761  0.762  0.762  0.761  1.517  0.761  0.761  0.762
## [17481]  0.761  0.761  0.762  1.516  0.761  1.211  0.762  1.367  0.762  0.761
## [17491]  1.100  1.512  0.761  1.392  0.761  0.761  0.957  0.762  0.761  1.528
## [17501]  1.198  1.171  0.761  0.762  1.285  0.819  0.761  0.762  0.999  1.051
## [17511]  1.527  0.762  1.527  0.762  0.761  1.311  0.761  0.762  0.761  0.761
## [17521]  1.078  1.062  0.761  0.761  1.363  1.524  0.761  0.761  1.229  1.522
## [17531]  0.761  1.524  0.761  0.824  1.329  1.510  0.761  0.761  0.762  0.762
## [17541]  1.431  0.761  0.762  0.762  1.636  1.491  0.762  1.507  0.762  1.017
## [17551]  1.094  0.761  0.762  1.115  0.762  1.267  1.516  1.944  1.505  0.762
## [17561]  0.762  1.517  0.761  0.762  0.762  1.524  0.761  1.268  0.762  0.762
## [17571]  0.761  0.762  0.762  0.762  0.761  1.513  0.762  1.523  1.446  0.762
## [17581]  0.762  2.131  0.761  0.762  0.762  1.500  0.762  0.762  1.169  0.761
## [17591]  1.090  0.762  1.256  0.904  0.762  1.523  1.517  0.761  0.761  0.761
## [17601]  0.761  0.761  0.761  1.202  0.761  0.761  1.061  1.321  1.393  0.761
## [17611]  0.871  0.761  1.272  1.466  0.761  1.376  2.139  1.287  1.806  0.761
## [17621]  0.761  1.432  2.278  0.761  1.341  2.942  0.762  0.761  1.497  2.283
## [17631]  0.761  1.527  1.028  1.920  1.416  1.985  0.762  0.761  0.761  1.209
## [17641]  1.517  0.761  0.761  1.131  0.762  0.761  0.761  0.761  0.761  0.761
## [17651]  0.762  1.517  1.509  0.761  0.761  0.762  0.761  1.518  1.082  0.761
## [17661]  0.762  1.751  0.762  0.761  0.762  0.761  0.762  0.761  0.761  0.762
## [17671]  1.324  0.762  1.835  0.762  0.762  0.762  0.762  0.762  1.483  0.762
## [17681]  0.761  0.762  0.761  0.762  1.469  0.762  1.517  0.762  1.163  0.949
## [17691]  0.761  0.888  1.483  0.762  0.762  0.762  1.476  0.762  1.606  0.762
## [17701]  1.663  1.528  0.761  1.114  1.754  0.762  1.527  0.936  2.255  0.761
## [17711]  1.987  0.761  3.135  1.144  1.931  1.272  0.761  0.762  1.339  0.762
## [17721]  0.886  1.487  2.576  1.425  1.902  1.395  2.239  0.762  0.761  2.182
## [17731]  1.434  1.950  2.313  0.762  1.411  2.120  0.761  1.802  1.528  0.762
## [17741]  0.761  0.761  1.238  0.761  0.761  0.761  0.761  0.761  0.761  0.762
## [17751]  1.351  0.761  0.762  0.762  0.762  0.761  0.762  1.414  0.761  0.762
## [17761]  1.518  0.761  0.761  0.762  0.762  0.761  0.761  0.761  0.762  0.761
## [17771]  0.761  1.172  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.762
## [17781]  1.242  0.761  1.285  1.774  0.762  0.761  0.761  1.154  0.762  0.761
## [17791]  0.761  0.762  0.762  1.526  0.761  0.762  0.762  1.466  0.761  1.095
## [17801]  0.762  0.761  1.345  0.762  0.761  1.756  0.761  0.762  0.762  1.193
## [17811]  1.864  0.762  1.501  1.071  0.761  0.958  1.438  1.370  1.734  0.762
## [17821]  0.762  1.526  2.419  0.762  0.762  1.509  2.256  0.762  0.761  0.762
## [17831]  1.313  1.311  0.762  0.762  1.828  0.761  0.762  1.527  0.978  1.396
## [17841]  0.762  1.489  2.019  1.552  0.762  1.895  1.807  0.762  2.140  1.518
## [17851]  1.500  0.976  0.761  0.762  0.762  0.761  1.506  0.761  1.846  0.761
## [17861]  0.761  1.499  1.781  0.761  2.106  0.761  0.761  0.906  1.953  1.524
## [17871]  0.761  1.492  1.086  0.761  0.761  1.834  0.761  2.236  2.171  0.761
## [17881]  0.761  1.325  2.256  1.327  1.913  0.761  0.761  1.506  0.761  0.761
## [17891]  1.447  1.850  0.761  0.761  1.458  3.027  0.762  1.485  2.218  1.419
## [17901]  1.249  2.290  2.032  0.761  1.527  2.181  0.761  1.479  1.522  0.762
## [17911]  0.761  0.761  1.878  1.896  0.761  1.051  1.198  1.484  0.761  0.761
## [17921]  0.761  0.761  0.762  0.761  0.762  0.762  0.761  0.762  0.761  0.761
## [17931]  0.762  0.761  0.762  0.762  0.761  0.762  1.510  0.761  0.762  0.761
## [17941]  0.761  0.762  1.410  1.468  0.761  0.762  1.041  0.762  0.761  0.762
## [17951]  0.761  0.762  0.761  0.762  1.198  0.762  0.761  0.762  0.761  0.762
## [17961]  0.761  0.762  1.374  0.762  0.761  0.762  0.761  0.762  0.761  1.426
## [17971]  0.761  1.077  0.761  0.761  1.424  0.761  0.761  1.522  0.902  0.762
## [17981]  0.762  0.761  2.384  1.389  0.762  0.761  0.762  0.762  1.190  0.761
## [17991]  0.762  0.762  0.762  1.810  0.762  1.180  0.762  1.088  1.429  0.761
## [18001]  0.762  0.761  1.526  1.405  0.762  1.524  1.232  0.762  1.602  1.523
## [18011]  1.374  0.762  2.134  0.762  0.761  0.762  0.762  0.762  0.761  1.402
## [18021]  0.762  0.762  1.457  0.761  1.160  2.279  0.762  1.519  1.941  0.905
## [18031]  0.895  1.424  1.518  1.286  0.762  1.505  1.532  2.400  2.278  1.476
## [18041]  0.762  0.761  0.762  1.527  1.325  1.988  1.392  0.761  2.274  0.761
## [18051]  1.343  0.761  2.705  0.761  1.944  1.516  1.047  2.351  1.463  1.176
## [18061]  1.321  0.761  0.845  1.522  1.449  0.761  1.523  1.467  1.425  1.475
## [18071]  1.123  2.342  1.510  1.373  1.958  0.761  1.438  1.311  1.017  1.528
## [18081]  0.945  1.511  1.886  1.411  1.367  0.762  1.028  0.761  0.762  1.437
## [18091]  2.794  1.341  0.762  0.762  0.761  1.260  0.762  0.762  2.263  0.761
## [18101]  0.761  1.496  1.492  0.761  1.374  1.726  0.762  0.761  0.761  0.762
## [18111]  0.761  1.350  0.761  0.761  1.521  0.761  1.517  0.761  0.762  0.761
## [18121]  0.762  0.761  1.284  0.761  0.761  0.933  0.761  0.761  1.180  0.761
## [18131]  0.761  0.762  0.761  0.761  1.065  0.761  0.761  1.496  1.411  0.761
## [18141]  0.761  0.826  0.761  0.761  1.097  0.761  0.761  0.761  0.762  0.761
## [18151]  0.761  0.761  0.762  0.761  0.761  1.278  0.762  0.761  0.761  1.260
## [18161]  0.762  0.761  0.761  1.507  0.762  0.761  0.761  1.517  0.762  0.761
## [18171]  0.761  1.104  0.762  0.761  0.761  0.762  0.761  0.761  0.910  0.762
## [18181]  0.761  0.761  0.761  1.446  1.465  0.761  1.407  0.762  0.761  1.763
## [18191]  0.761  0.762  0.761  1.680  2.169  0.762  0.761  0.762  2.056  1.411
## [18201]  0.761  0.762  1.637  0.761  0.761  1.527  0.761  0.761  0.761  1.322
## [18211]  2.267  0.761  0.761  1.401  0.762  0.761  0.761  0.761  0.762  0.761
## [18221]  0.761  0.761  1.171  0.761  0.761  1.387  0.762  0.761  0.761  1.667
## [18231]  0.762  0.761  0.761  1.507  1.315  0.761  0.761  1.104  0.761  0.761
## [18241]  0.761  1.228  1.111  0.761  0.761  1.198  1.795  0.761  0.761  1.278
## [18251]  0.761  0.761  0.761  2.176  0.761  0.761  0.761  0.761  0.761  0.761
## [18261]  0.761  0.761  0.761  1.938  0.761  0.761  0.761  1.039  0.761  0.761
## [18271]  0.761  0.761  1.526  0.761  0.761  1.929  1.475  0.761  0.761  1.268
## [18281]  0.761  0.761  0.761  1.404  0.761  0.761  0.761  1.299  0.761  0.761
## [18291]  0.761  1.368  2.170  0.761  1.380  1.080  2.852  0.761  1.396  2.239
## [18301]  0.761  0.761  1.744  2.031  0.761  0.761  2.272  0.761  0.761  1.372
## [18311]  1.375  0.761  0.761  1.269  1.138  0.761  0.761  0.761  2.199  0.761
## [18321]  0.761  1.522  0.761  0.761  1.497  0.761  1.216  0.761  1.521  1.509
## [18331]  0.761  1.506  0.762  1.419  0.761  0.762  0.887  1.032  0.762  1.894
## [18341]  0.762  0.762  1.526  1.176  0.762  1.475  0.761  0.762  0.761  0.761
## [18351]  0.762  0.761  0.761  0.762  0.761  0.761  0.762  0.762  0.963  1.507
## [18361]  0.762  0.762  0.762  0.762  0.762  1.810  0.762  1.248  0.762  1.397
## [18371]  0.762  0.761  0.762  0.762  1.518  0.762  0.762  1.368  0.762  0.762
## [18381]  1.045  0.761  0.762  0.762  0.761  0.761  0.762  0.762  0.761  1.497
## [18391]  0.762  0.762  1.183  0.762  1.518  0.762  2.116  0.762  0.761  1.527
## [18401]  0.829  0.762  0.761  1.520  1.176  0.762  0.761  0.762  0.762  1.425
## [18411]  0.761  0.762  1.268  0.761  1.386  0.762  1.379  1.315  0.762  0.762
## [18421]  0.761  0.762  1.497  1.129  0.761  0.762  1.447  1.465  1.231  0.761
## [18431]  1.261  0.761  2.411  0.762  0.762  2.157  0.761  1.170  0.762  0.762
## [18441]  0.761  0.762  0.851  0.762  1.528  1.231  1.508  1.668  0.761  1.974
## [18451]  1.527  1.458  0.761  1.343  0.762  0.761  1.028  0.761  0.762  0.762
## [18461]  1.342  1.179  0.761  0.762  1.510  0.762  0.761  0.761  0.762  0.762
## [18471]  1.260  0.762  0.762  1.193  0.900  0.762  0.762  1.412  0.762  0.762
## [18481]  0.762  1.427  0.762  1.517  0.762  0.762  0.762  2.290  0.762  0.762
## [18491]  1.447  0.762  0.762  1.366  0.761  1.217  1.517  1.764  1.252  1.217
## [18501]  1.411  1.525  0.761  0.761  1.523  0.762  0.761  0.761  1.527  1.517
## [18511]  0.761  0.762  0.761  0.762  0.762  0.761  0.761  1.197  1.033  1.032
## [18521]  1.311  0.857  0.882  0.774  0.761  1.383  0.761  1.411  1.325  0.946
## [18531]  0.761  0.850  0.761  0.761  0.880  0.761  0.761  0.946  0.869  0.761
## [18541]  0.761  1.392  1.107  1.042  0.761  1.996  0.899  0.925  0.761  2.256
## [18551]  0.838  0.761  0.761  2.155  0.883  0.761  0.761  1.230  0.761  0.761
## [18561]  0.761  1.404  0.761  0.761  0.762  0.761  1.526  0.761  0.762  0.761
## [18571]  0.762  0.761  1.032  1.593  0.762  0.762  0.762  1.225  0.761  1.248
## [18581]  0.762  0.761  0.857  1.522  1.205  1.009  0.761  1.013  1.527  0.761
## [18591]  0.761  1.394  0.761  1.042  1.802  2.115  0.761  0.761  0.761  0.762
## [18601]  0.761  0.761  0.761  0.762  1.395  0.761  0.762  0.761  0.761  0.761
## [18611]  0.762  1.459  0.761  0.978  1.042  1.527  0.957  0.761  0.762  0.761
## [18621]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18631]  0.761  0.761  1.524  0.761  0.761  0.761  0.761  1.211  1.343  0.761
## [18641]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.822  0.761
## [18651]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18661]  1.163  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18671]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18681]  0.761  0.761  0.761  0.761  0.761  0.761  1.646  0.761  1.103  0.761
## [18691]  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761
## [18701]  1.197  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761  0.761
## [18711]  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.762  0.761  0.761
## [18721]  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761
## [18731]  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761
## [18741]  0.762  0.761  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.761
## [18751]  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18761]  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.761
## [18771]  0.761  0.987  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.922
## [18781]  0.761  0.761  0.761  2.120  0.761  0.761  0.761  0.762  0.761  0.761
## [18791]  0.762  0.761  0.761  0.762  0.761  1.399  0.762  0.761  0.761  0.762
## [18801]  0.761  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18811]  0.761  0.761  0.761  0.761  0.761  0.761  1.524  0.761  0.761  0.761
## [18821]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18831]  0.761  0.761  1.812  0.761  0.761  1.446  0.761  0.761  1.445  0.761
## [18841]  0.761  2.194  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18851]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761
## [18861]  0.761  1.489  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761
## [18871]  0.761  0.761  0.761  0.761  0.761  0.762  0.762  0.761  0.761  0.762
## [18881]  0.762  0.761  0.761  0.762  0.762  0.761  0.761  0.762  0.762  0.761
## [18891]  0.761  0.762  0.762  0.761  0.761  0.762  0.762  0.761  0.761  0.762
## [18901]  1.434  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.761
## [18911]  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762
## [18921]  0.761  0.761  0.761  0.762  0.761  0.761  0.761  1.371  1.528  0.761
## [18931]  0.761  0.762  0.762  0.761  0.761  0.762  0.762  0.761  0.761  0.762
## [18941]  1.210  0.761  0.761  1.382  0.761  0.761  0.761  0.761  0.761  0.761
## [18951]  0.761  0.761  0.761  0.761  0.761  1.483  0.761  0.761  0.761  0.762
## [18961]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18971]  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762
## [18981]  0.761  0.761  0.761  0.762  0.761  1.164  0.761  0.762  0.761  1.026
## [18991]  0.761  0.762  0.761  0.761  0.761  0.761  0.761  1.046  0.761  0.761
## [19001]  0.761  1.528  0.761  0.761  0.761  0.761  0.761  1.518  0.761  0.761
## [19011]  0.761  0.761  0.761  0.761  1.190  0.761  0.761  0.761  0.761  1.341
## [19021]  0.762  0.761  0.761  0.761  0.761  1.528  0.761  1.448  0.853  0.761
## [19031]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [19041]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [19051]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.904  0.761  0.761
## [19061]  0.761  1.341  0.761  0.761  0.761  1.503  0.761  0.761  0.762  0.762
## [19071]  0.761  0.761  0.762  0.762  0.761  0.761  0.761  0.762  0.761  0.761
## [19081]  0.762  0.762  0.761  0.761  0.761  0.762  0.761  0.762  0.761  0.761
## [19091]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761
## [19101]  0.762  0.762  0.761  0.761  0.762  0.761  0.761  0.762  0.761  0.761
## [19111]  0.762  0.761  0.761  0.761  0.976  0.761  0.761  0.761  0.920  0.761
## [19121]  0.761  0.761  0.761  0.761  1.053  0.761  0.761  2.232  1.521  0.761
## [19131]  1.767  1.519  0.761  1.862  1.392  0.761  0.762  2.180  1.508  0.761
## [19141]  1.301  1.520  0.761  2.582  0.761  0.761  1.522  0.761  0.761  1.519
## [19151]  1.505  0.761  0.761  1.769  0.761  1.523  1.470  1.516  0.761  1.474
## [19161]  0.761  1.448  0.761  1.181  2.145  0.761  0.761  1.970  0.761  0.762
## [19171]  1.467  2.186  1.491  1.517  1.386  1.528  0.761  1.507  1.522  0.761
## [19181]  0.761  1.517  1.517  0.761  2.167  1.847  0.761  0.761  0.761  0.761
## [19191]  0.761  0.761  0.761  0.761  1.520  0.761  0.761  0.761  0.761  0.761
## [19201]  0.761  1.386  0.761  0.761  1.260  0.761  1.389  0.890  0.761  1.527
## [19211]  1.157  0.761  0.864  0.762  1.117  0.761  0.761  0.899  0.761  0.761
## [19221]  0.762  1.709  0.761  0.761  0.762  0.762  0.761  0.761  1.269  0.761
## [19231]  0.761  0.761  1.426  0.761  0.761  0.762  0.762  0.848  0.761  0.762
## [19241]  1.655  0.761  0.761  1.020  1.357  0.761  0.761  0.761  1.335  0.761
## [19251]  1.506  0.761  1.758  0.761  1.467  0.761  1.040  1.486  1.293  0.761
## [19261]  0.904  0.762  0.762  1.411  0.761  0.762  1.241  0.762  1.683  0.762
## [19271]  0.761  0.761  1.106  0.897  1.492  1.523  1.370  0.761  1.431  0.761
## [19281]  1.170  0.761  0.762  0.761  1.520  0.761  1.424  0.761  1.484  1.524
## [19291]  0.761  0.761  1.517  0.762  1.062  1.528  0.762  0.762  1.525  1.509
## [19301]  2.275  1.526  1.500  1.410  0.761  0.761  0.762  0.761  1.232  0.762
## [19311]  1.380  0.761  0.761  1.183  1.505  0.761  0.761  1.140  0.761  1.504
## [19321]  1.972  1.519  1.497  0.761  0.761  1.358  0.761  1.444  1.343  0.761
## [19331]  1.474  1.510  1.473  0.761  0.762  1.037  1.266  0.761  1.470  0.761
## [19341]  1.417  0.761  1.050  0.761  0.762  0.761  0.848  0.761  0.762  0.761
## [19351]  1.130  0.761  1.373  0.761  1.230  1.505  1.373  1.509  1.417  1.528
## [19361]  0.762  1.523  1.516  1.268  1.420  1.430  1.984  0.762  0.762  1.383
## [19371]  1.524  1.341  1.626  0.761  1.527  1.410  1.763  1.462  1.467  0.762
## [19381]  0.762  1.424  1.446  0.761  1.468  1.482  0.762  0.761  0.761  1.290
## [19391]  0.762  1.474  0.761  0.762  1.412  0.761  0.761  0.762  0.761  0.762
## [19401]  0.761  0.762  0.761  1.462  0.761  0.761  0.761  1.451  0.761  0.762
## [19411]  0.761  1.410  1.290  0.761  0.762  0.761  0.762  0.761  0.762  0.761
## [19421]  0.762  0.761  0.762  0.761  0.762  0.761  0.762  1.032  0.762  0.762
## [19431]  0.761  2.075  0.762  0.762  0.762  0.761  0.761  0.762  0.762  0.762
## [19441]  0.762  0.762  0.762  0.761  0.761  1.525  1.503  0.761  0.761  1.322
## [19451]  1.362  1.306  1.553  1.423  0.761  1.516  0.761  0.819  0.761  0.762
## [19461]  0.850  0.762  0.761  0.762  0.761  0.762  0.761  0.762  0.761  0.762
## [19471]  0.761  0.762  0.761  0.762  0.761  0.762  0.761  0.762  0.761  0.762
## [19481]  0.761  0.762  0.762  0.761  0.762  0.761  0.762  0.761  0.761  0.761
## [19491]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.762  0.761
## [19501]  0.761  0.762  1.197  0.761  0.761  0.762  0.762  0.761  0.761  0.761
## [19511]  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761
## [19521]  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761
## [19531]  0.762  0.761  1.523  0.761  1.485  0.761  1.522  0.761  0.761  0.761
## [19541]  0.761  0.761  0.761  1.049  0.761  0.761  0.827  1.374  1.479  0.762
## [19551]  1.634  0.761  1.351  1.512  0.761  1.392  1.311  1.232  1.941  1.506
## [19561]  1.351  1.490  1.900  0.761  0.762  1.527  1.508  1.510  0.762  0.761
## [19571]  1.474  1.341  0.888  0.761  0.762  1.374  0.762  0.761  0.762  0.761
## [19581]  1.523  0.761  0.762  1.475  1.446  0.761  0.762  0.761  0.826  0.761
## [19591]  1.170  0.762  0.762  0.761  2.459  1.526  0.761  1.965  0.761  0.761
## [19601]  0.761  1.452  0.761  1.516  0.761  0.761  1.411  0.761  0.761  0.761
## [19611]  0.761  0.761  1.164  0.761  0.761  1.497  0.761  1.524  0.976  0.761
## [19621]  0.761  1.394  1.496  0.761  0.761  1.860  0.761  0.761  0.761  1.247
## [19631]  0.761  1.814  1.482  0.761  0.761  0.762  1.442  1.412  0.761  0.762
## [19641]  0.761  1.517  0.762  0.762  0.761  1.237  2.142  1.439  0.762  0.761
## [19651]  0.761  1.151  0.761  1.457  0.761  0.762  0.762  0.762  0.761  0.761
## [19661]  0.762  1.133  0.761  0.761  0.762  1.009  0.761  0.761  1.093  0.761
## [19671]  0.761  0.853  0.761  0.761  0.762  1.181  0.761  0.762  0.762  0.761
## [19681]  0.761  0.762  0.762  0.761  0.761  0.762  0.762  0.761  0.761  0.762
## [19691]  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761
## [19701]  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761
## [19711]  0.762  0.761  0.761  0.761  0.762  1.042  0.761  0.761  0.762  0.761
## [19721]  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761  0.761
## [19731]  0.762  0.761  0.761  0.762  0.762  1.462  0.761  0.761  0.762  0.761
## [19741]  1.351  0.761  0.761  0.761  0.905  0.761  0.761  0.761  0.761  1.487
## [19751]  0.761  1.464  0.761  0.761  1.217  1.341  1.513  1.527  1.479  0.761
## [19761]  1.230  0.762  1.293  1.362  1.315  1.411  0.761  0.762  1.383  1.398
## [19771]  1.090  1.400  1.186  0.761  1.454  0.761  1.353  1.474  0.761  1.059
## [19781]  1.374  0.761  0.761  0.761  1.411  0.761  0.761  0.761  0.761  0.761
## [19791]  0.761  0.761  1.502  0.761  0.761  1.464  0.761  0.761  0.761  0.761
## [19801]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  1.411
## [19811]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [19821]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762
## [19831]  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761  0.762  0.762
## [19841]  0.761  0.761  0.762  0.761  0.761  0.762  0.762  0.761  0.761  0.762
## [19851]  0.761  0.761  0.761  0.762  0.761  0.761  1.497  0.762  0.761  0.761
## [19861]  1.374  0.761  0.761  1.993  0.761  0.761  0.762  0.900  0.761  0.761
## [19871]  0.762  1.419  0.761  0.761  0.762  0.762  0.761  0.761  0.762  0.762
## [19881]  0.761  0.761  1.118  0.762  0.761  0.761  0.762  0.762  0.761  0.761
## [19891]  0.762  0.762  0.761  0.761  0.762  0.762  0.761  0.761  0.762  0.762
## [19901]  0.761  0.761  0.762  0.762  0.761  0.761  0.762  0.762  0.761  0.761
## [19911]  0.762  1.258  0.761  0.761  1.504  1.429  0.761  0.761  1.511  0.761
## [19921]  1.476  0.761  0.762  1.390  0.762  0.761  0.761  1.133  1.316  0.761
## [19931]  0.888  0.848  0.761  1.257  0.762  1.117  1.334  0.762  0.762  0.762
## [19941]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [19951]  1.380  1.217  0.762  0.762  0.762  0.762  0.762  0.762  0.762  1.629
## [19961]  0.761  0.762  1.272  0.761  0.762  0.762  1.003  0.808  0.762  0.762
## [19971]  0.761  0.762  0.762  0.762  1.526  0.761  0.762  0.762  0.762  0.761
## [19981]  0.762  0.762  1.411  0.762  0.761  0.762  0.762  0.761  0.762  0.762
## [19991]  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762
## [20001]  1.216  0.761  0.762  0.762  1.323  0.761  0.762  0.762  1.343  0.762
## [20011]  0.762  0.762  0.761  1.464  1.503  0.761  1.396  0.762  1.225  1.524
## [20021]  1.467  0.762  0.761  1.521  1.411  1.523  0.761  1.790  0.761  1.452
## [20031]  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761
## [20041]  0.761  0.762  0.762  0.761  0.761  0.762  1.343  0.761  0.761  0.762
## [20051]  0.762  0.762  0.762  0.762  0.762  0.762  0.761  0.761  0.761  0.761
## [20061]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20071]  0.761  0.761  0.761  0.836  0.768  0.868  0.764  0.762  0.762  0.771
## [20081]  0.761  0.762  0.778  0.762  0.762  0.782  0.762  0.778  0.762  0.798
## [20091]  0.762  0.828  0.830  0.816  0.762  0.779  0.762  0.802  0.821  0.761
## [20101]  0.804  0.761  0.824  0.761  0.815  0.761  0.794  0.761  0.782  0.761
## [20111]  0.783  0.761  0.784  0.761  0.787  0.761  0.797  0.761  0.791  0.761
## [20121]  0.798  0.761  0.790  0.761  0.789  0.761  0.767  0.761  0.761  0.761
## [20131]  0.761  0.761  0.761  0.837  0.761  0.767  0.761  0.764  0.761  0.787
## [20141]  0.780  0.782  0.788  0.765  0.779  0.770  0.792  0.773  0.780  0.765
## [20151]  0.777  0.767  0.779  0.765  0.777  0.765  0.778  0.767  0.773  0.766
## [20161]  0.772  0.772  0.774  0.766  0.772  0.771  0.794  0.774  0.778  0.766
## [20171]  0.773  0.762  0.775  0.762  0.778  0.762  0.792  0.795  0.761  0.761
## [20181]  0.761  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.762  0.761
## [20191]  0.761  0.761  0.761  0.761  0.762  0.762  0.761  0.761  0.762  0.761
## [20201]  0.761  0.761  0.761  0.762  0.761  0.761  0.762  0.761  0.762  0.761
## [20211]  1.032  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20221]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20231]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20241]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20251]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20261]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20271]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20281]  0.761  0.762  0.771  0.762  0.762  0.762  0.762  0.773  0.762  0.762
## [20291]  0.762  0.762  0.762  0.857  0.762  0.762  0.821  0.762  0.793  0.762
## [20301]  0.766  0.762  0.764  0.761  0.761  0.761  0.763  0.761  0.761  0.761
## [20311]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20321]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20331]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20341]  0.761  0.761  0.761  0.761  0.761  0.761  0.791  0.841  0.762  0.827
## [20351]  0.816  0.762  0.817  0.866  0.762  0.857  0.815  0.762  0.803  0.761
## [20361]  0.762  0.816  0.792  0.762  0.830  0.760  0.762  0.823  0.762  0.816
## [20371]  0.762  0.810  0.762  0.785  0.762  0.781  0.762  0.811  0.761  0.815
## [20381]  0.761  0.810  0.761  0.814  0.761  0.791  0.761  0.786  0.761  0.795
## [20391]  0.761  0.801  0.761  0.789  0.761  0.806  0.761  0.796  0.761  0.788
## [20401]  0.761  0.792  0.761  0.791  0.761  0.798  0.761  0.761  0.761  0.761
## [20411]  0.761  0.761  0.855  0.761  0.799  0.761  0.773  0.761  0.765  0.761
## [20421]  0.788  0.761  0.768  0.761  0.773  0.761  0.799  0.761  0.781  0.804
## [20431]  0.761  0.768  0.761  0.783  0.761  0.804  0.799  0.771  0.761  0.789
## [20441]  0.761  0.793  0.779  0.761  0.775  0.761  0.786  0.761  0.799  0.827
## [20451]  0.761  0.762  0.763  0.762  0.763  0.762  0.762  0.763  0.761  0.762
## [20461]  0.761  0.762  0.762  0.762  0.761  0.762  0.764  0.762  0.762  0.761
## [20471]  0.764  0.762  0.761  0.763  0.762  0.761  0.761  0.762  0.761  0.761
## [20481]  0.762  0.763  0.762  0.762  0.762  0.761  0.762  0.762  0.761  0.761
## [20491]  0.761  0.762  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.762
## [20501]  0.762  0.761  0.762  0.761  0.761  0.762  0.762  0.761  0.762  0.761
## [20511]  0.761  0.761  0.763  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20521]  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.761  0.761  0.781
## [20531]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20541]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.768
## [20551]  0.762  0.761  0.761  0.762  0.763  0.761  0.762  0.761  0.761  0.761
## [20561]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20571]  0.761  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.762
## [20581]  0.762  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.761  0.763
## [20591]  0.761  0.761  0.761  0.761  0.762  0.762  0.762  0.762  0.762  0.762
## [20601]  0.762  0.762  0.762  0.762  0.762  0.762  0.761  0.761  0.761  0.761
## [20611]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20621]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20631]  0.762  0.761  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.762
## [20641]  0.761  0.761  0.762  0.761  0.761  0.762  0.761  0.760  0.762  0.761
## [20651]  0.761  0.762  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.762
## [20661]  0.762  0.761  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.761
## [20671]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20681]  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20691]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20701]  0.823  0.879  0.934  0.910  0.975  0.874  0.994  1.192  1.321  0.876
## [20711]  1.038  0.953  0.761  0.912  0.761  0.834  0.761  0.817  0.925  0.859
## [20721]  1.156  1.589  1.320  1.063  0.778  0.777  0.777  0.781  0.782  0.786
## [20731]  0.761  1.191  1.054  1.080  1.053  0.963  0.912  0.964  1.086  1.111
## [20741]  0.868  1.135  0.770  1.059  0.879  0.797  0.869  1.038  0.786  0.869
## [20751]  1.038  0.786  1.084  0.905  0.987  1.055  1.109  1.047  0.765  0.761
## [20761]  0.761  0.761  0.761  0.761  0.761  0.762  1.116  1.479  0.890  1.234
## [20771]  1.203  0.839  0.761  0.762  0.761  0.761  0.762  0.762  0.762  0.761
## [20781]  0.761  1.936  1.062  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20791]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  1.197
## [20801]  1.925  1.316  0.769  0.761  0.761  0.771  0.762  0.761  0.761  0.761
## [20811]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20821]  0.761  0.761  0.761  0.762  0.762  0.762  0.853  0.762  0.762  0.825
## [20831]  0.831  0.815  0.823  0.792  0.771  0.761  0.761  0.763  0.764  1.960
## [20841]  0.761  0.761  0.761  0.761  1.026  2.246  0.761  0.980  1.027  0.761
## [20851]  0.761  1.022  0.761  0.808  0.882  0.987  0.887  0.802  0.914  0.761
## [20861]  1.041  2.264  1.097  1.104  0.930  2.437  0.932  0.904  1.239  0.761
## [20871]  1.154  0.761  0.764  0.936  0.921  0.762  0.761  0.761  0.761  0.761
## [20881]  0.761  0.761  0.761  0.765  0.765  0.765  0.765  0.765  0.761  0.846
## [20891]  0.845  0.809  0.802  0.801  1.149  1.897  0.761  1.030  1.114  0.870
## [20901]  0.888  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.830
## [20911]  1.195  1.058  0.888  0.792  0.763  0.761  0.761  0.762  0.779  0.761
## [20921]  0.761  0.760  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20931]  0.762  0.761  1.025  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20941]  0.761  0.761  0.761  0.765  0.765  0.933  0.828  0.824  0.786  0.761
## [20951]  0.761  0.761  0.761  0.761  0.761  0.905  1.215  2.211  1.042  1.600
## [20961]  2.753  0.761  1.033  0.823  0.761  0.761  1.969  1.072  1.040  0.969
## [20971]  0.890  0.775  0.970  1.309  1.190  0.761  1.310  1.317  0.761  0.968
## [20981]  1.060  0.761  0.761  1.312  1.298  1.196  0.762  0.939  1.206  1.045
## [20991]  1.150  0.971  1.044  1.167  0.782  1.312  1.366  0.761  2.554  1.397
## [21001]  1.087  1.370  1.199  1.383  0.795  1.155  1.400  1.401  1.423  1.226
## [21011]  1.380  1.366  1.397  1.407  1.415  1.420  1.413  1.315  1.093  1.048
## [21021]  1.097  1.073  1.086  1.113  1.075  1.146  1.116  1.089  1.094  1.131
## [21031]  1.131  1.078  1.157  1.166  1.047  1.083  1.089  1.104  1.171  1.175
## [21041]  1.268  1.400  1.095  1.084  1.084  0.927  0.912  1.122  1.114  1.071
## [21051]  1.068  1.069  1.044  1.039  1.032  1.065  0.819  0.879  0.922  0.927
## [21061]  1.054  0.799  0.919  0.937  0.793  0.891  0.800  0.843  0.907  0.882
## [21071]  0.881  0.926  0.856  0.812  0.777  0.790  0.763  0.767  0.788  0.779
## [21081]  0.764  0.761  0.846  0.790  0.795  0.791  0.830  0.761  0.880  0.846
## [21091]  0.927  0.987  0.938  0.835  0.904  0.812  0.873  0.815  0.876  0.837
## [21101]  0.761  0.945  0.978  0.863  0.872  0.866  0.917  0.868  0.936  0.864
## [21111]  0.910  0.948  0.858  0.837  0.922  0.871  0.795  0.845  0.832  0.785
## [21121]  0.798  0.781  0.761  0.766  0.761  0.790  0.761  0.801  0.761  0.781
## [21131]  0.779  0.770  0.784  0.858  0.774  0.761  0.773  0.761  0.842  0.921
## [21141]  0.761  0.794  0.783  0.878  0.761  0.766  0.827  0.777  0.761  0.796
## [21151]  0.761  0.778  0.761  0.761  0.761  0.781  0.761  0.762  0.761  0.782
## [21161]  0.803  0.762  0.779  0.807  0.836  0.783  1.132  0.809  0.773  0.803
## [21171]  0.890  0.791  1.145  0.763  0.989  0.778  0.761  0.761  0.762  0.762
## [21181]  0.761  1.315  1.081  1.091  1.091  1.080  1.073  1.092  1.083  1.067
## [21191]  1.083  1.082  1.012  1.085  1.078  1.081  1.145  1.102  1.085  1.080
## [21201]  1.075  1.059  1.070  1.082  1.068  1.351  1.199  1.230  1.206  1.183
## [21211]  1.088  1.182  1.076  1.037  0.940  0.874  0.829  1.077  1.370  1.101
## [21221]  1.056  1.077  1.055  1.121  1.131  0.828  0.816  1.214  1.122  1.092
## [21231]  1.065  1.073  1.062  1.076  1.075  1.084  0.843  0.765  0.762  0.761
## [21241]  0.761  0.761  0.761  0.761  0.761  0.765  0.762  0.790  0.805  0.761
## [21251]  0.761  0.810  0.761  0.801  0.798  0.802  0.761  0.805  0.761  0.761
## [21261]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [21271]  0.761  0.761  0.797  0.761  0.855  0.874  0.819  0.817  0.762  0.899
## [21281]  0.921  0.762  0.791  0.762  0.763  0.762  0.911  0.764  0.816  0.762
## [21291]  0.783  0.780  0.784  0.838  0.784  0.768  0.791  0.761  0.777  0.761
## [21301]  0.761  0.764  0.767  0.878  0.789  0.807  0.761  0.806  0.821  0.763
## [21311]  0.808  0.791  0.761  0.850  0.761  0.912  0.953  0.857  0.870  0.782
## [21321]  0.794  0.763  0.763  0.765  0.763  0.869  0.998  0.761  1.514  1.166
## [21331]  1.103  0.761  0.991  0.827  0.811  0.968  1.071  0.896  0.791  0.791
## [21341]  0.799  0.799  0.983  0.763  0.763  1.064  1.172  0.762  0.762  1.154
## [21351]  0.762  0.762  1.145  0.762  0.762  1.221  0.762  1.028  0.926  1.094
## [21361]  0.778  1.232  0.987  1.093  1.056  1.280  1.041  0.762  1.124  1.108
## [21371]  0.995  1.179  1.121  1.184  1.142  1.203  0.761  1.214  1.191  0.761
## [21381]  1.347  0.761  0.761  0.761  1.143  0.761  1.071  1.058  1.125  1.011
## [21391]  1.235  1.188  1.022  0.831  1.354  1.041  1.140  1.434  1.333  1.435
## [21401]  0.923  0.762  0.916  0.762  0.925  0.762  0.893  0.762  0.762  0.762
## [21411]  0.879  0.762  0.762  0.762  0.885  0.762  0.762  0.762  0.832  0.762
## [21421]  0.762  0.762  0.761  0.762  0.762  0.761  0.761  0.761  0.762  0.762
## [21431]  0.842  0.763  0.762  0.762  0.761  0.761  0.762  0.762  0.761  0.761
## [21441]  0.762  0.768  0.761  0.761  0.762  0.762  0.761  0.761  0.762  0.868
## [21451]  0.761  0.761  0.762  0.762  0.761  0.761  0.762  0.761  0.761  0.761
## [21461]  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.777
## [21471]  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761
## [21481]  0.762  0.976  0.761  0.761  0.762  0.824  0.761  0.761  0.762  0.761
## [21491]  0.761  0.761  0.762  0.761  0.761  0.761  0.762  1.050  0.761  0.761
## [21501]  0.762  0.914  0.761  0.761  0.762  0.762  0.761  0.761  0.803  0.762
## [21511]  0.761  0.761  0.762  0.761  0.761  1.030  0.777  0.761  0.761  0.761
## [21521]  0.762  0.761  0.761  0.859  0.761  0.761  0.761  0.925  0.766  0.761
## [21531]  0.761  0.805  0.788  0.952  0.761  0.761  0.761  0.762  0.830  0.761
## [21541]  0.930  0.761  0.762  0.861  0.761  1.046  0.761  0.762  0.838  0.761
## [21551]  1.029  0.761  0.762  0.900  0.761  1.067  0.762  0.762  0.813  0.761
## [21561]  1.032  0.761  0.852  0.761  1.029  0.761  0.860  0.761  0.812  0.762
## [21571]  0.855  0.761  0.826  0.762  0.851  0.762  0.783  0.762  0.762  0.808
## [21581]  0.761  0.761  0.762  0.762  0.767  0.762  0.761  0.762  0.763  0.761
## [21591]  0.767  0.762  0.763  0.791  0.761  0.762  0.787  0.761  0.762  0.761
## [21601]  0.761  0.762  0.786  0.761  0.761  0.762  0.762  0.761  0.761  0.762
## [21611]  0.761  0.761  0.761  0.762  0.760  0.761  0.761  0.762  0.761  0.761
## [21621]  0.761  0.762  0.769  0.761  0.761  0.762  0.761  0.761  0.761  0.762
## [21631]  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.762  1.421
## [21641]  0.761  1.258  0.761  0.761  1.062  0.761  0.761  1.560  0.761  0.761
## [21651]  0.883  0.761  0.761  1.490  0.761  0.761  1.740  0.761  0.761  1.594
## [21661]  0.761  0.761  0.914  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [21671]  0.761  0.761  0.761  1.139  1.160  0.761  0.761  1.251  0.761  0.761
## [21681]  1.320  0.761  0.761  1.086  0.873  0.761  0.761  0.761  0.761  1.480
## [21691]  0.761  0.761  1.173  0.761  0.761  0.965  0.856  0.761  1.231  0.839
## [21701]  0.761  1.138  0.761  0.761  1.713  0.761  0.821  1.477  1.220  0.761
## [21711]  1.239  0.761  0.761  1.096  0.761  0.761  0.968  0.761  0.761  1.116
## [21721]  0.761  0.761  0.960  0.761  0.761  0.905  0.761  0.761  0.762  0.761
## [21731]  0.761  0.928  0.761  0.761  0.762  0.815  0.762  0.761  0.761  0.762
## [21741]  0.761  0.761  0.762  0.761  0.762  0.761  0.762  0.763  0.761  1.044
## [21751]  0.762  0.761  1.015  0.762  0.761  1.062  0.761  0.761  0.772  0.927
## [21761]  1.053  1.366  0.761  1.089  0.761  0.761  0.856  0.761  1.385  0.761
## [21771]  0.976  0.761  0.761  0.980  0.761  0.761  0.761  0.761  1.039  0.761
## [21781]  0.761  0.966  0.761  0.761  1.382  0.761  0.761  0.764  0.763  0.769
## [21791]  0.768  0.767  0.763  0.768  0.767  0.763  0.766  0.767  0.768  0.765
## [21801]  0.768  0.769  0.764  0.762  0.764  0.769  0.775  0.777  0.774  0.764
## [21811]  0.763  0.763  0.765  0.776  0.768  0.766  0.766  0.769  0.767  0.771
## [21821]  0.769  0.771  0.774  0.783  0.771  0.777  0.771  0.770  0.773  0.769
## [21831]  0.768  0.771  0.768  0.782  0.769  0.768  0.770  0.771  0.770  0.767
## [21841]  0.933  0.984  0.767  0.766  0.770  0.771  0.767  0.771  0.812  0.772
## [21851]  0.774  0.776  0.772  0.771  0.772  0.772  0.780  0.774  0.772  0.777
## [21861]  0.775  0.779  0.774  0.784  0.784  0.782  0.778  0.790  0.786  0.795
## [21871]  0.804  0.787  0.774  0.766  0.767  0.767  0.768  0.768  0.768  0.766
## [21881]  0.783  0.882  0.942  0.937  0.879  0.880  0.868  0.810  0.885  0.810
## [21891]  0.895  0.858  0.859  0.861  0.841  0.856  0.851  0.802  0.827  0.861
## [21901]  0.878  0.819  0.849  0.837  0.805  0.822  0.804  0.806  0.824  0.856
## [21911]  0.851  0.809  0.801  0.841  4.832  0.814  1.640  3.098  2.500  1.902
## [21921]  1.418  1.149  3.463  3.322  2.462  4.689  2.104  3.951  1.163  1.916
## [21931]  5.188  9.283  5.285  7.480  5.268  4.734  2.401  1.368  6.784  7.548
## [21941]  8.372  7.651  3.549  2.562  3.470  4.713  3.940  4.456  2.341  0.800
## [21951]  1.035  1.765  2.084  0.761  2.041  2.859  1.486  1.236  1.462  2.585
## [21961]  2.550  1.069  1.470  2.396  1.507  2.935  2.560  1.376  2.506  2.977
## [21971]  3.008  2.307  2.109  1.194  1.323  2.921  2.733  1.399  1.146  0.761
## [21981]  0.762  0.762  0.762  0.761  0.773  4.751  0.840  1.325  1.318  3.289
## [21991]  1.365  2.159  1.689  1.339  1.366  1.519  0.761  1.947  0.761  3.015
## [22001]  1.344  1.915  1.304  1.377  2.112  1.964  2.690  2.701  1.327  2.051
## [22011]  2.335  1.637  1.938  1.819  2.169  1.678  2.958  2.479  2.556  2.553
## [22021]  1.994  2.595  2.454  2.907  3.825  3.501  3.662  2.434  1.626  2.087
## [22031]  1.935  1.374  1.299  1.316  1.357  1.370  2.410 11.112  1.002  1.747
## [22041]  0.987  3.289  0.761  0.766  0.769  0.767  0.761  1.641  0.764  0.779
## [22051]  0.761  0.777  0.761  0.799  0.761  0.865  0.761  0.776  0.791  0.812
## [22061]  0.796  0.804  0.769  0.787  0.762  0.778  0.761  0.768  0.761  0.773
## [22071]  0.761  0.761  0.762  0.763  0.761  0.777  0.761  0.772  0.761  0.770
## [22081]  0.762  0.770  0.890  0.767  0.767  0.777  0.789  0.791  0.771  0.776
## [22091]  0.801  0.789  0.761  0.790  0.766  0.770  0.766  1.106  0.763  0.761
## [22101]  0.761  0.761  0.761  0.762  0.761  0.762  0.942  0.808  0.818  0.781
## [22111]  0.774  0.770  0.763  0.762  0.762  3.364  2.305  3.743  1.334  0.866
## [22121]  1.242  2.620  1.318  1.350  4.056 14.143  0.761  0.764  0.761  0.761
## [22131]  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [22141]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762
## [22151]  0.761  0.761  0.763  0.761  0.761  0.761  0.762  0.765  0.763  0.761
## [22161]  0.763  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.761
## [22171]  0.762  0.761  0.761  0.762  0.761  0.768  0.761  0.762  0.761  0.762
## [22181]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.831  0.761  0.761
## [22191]  0.763  0.762  0.761  0.762  0.761  0.761  0.761  0.762  0.762  0.761
## [22201]  0.761  0.761  0.762  0.762  0.761  0.761  0.761  0.761  0.761  0.761
## [22211]  0.763  0.761  0.761  0.953  1.248  2.270  0.880  1.280  0.948  0.852
## [22221]  0.866  0.927  1.219  1.140  0.906  1.693  1.397  1.496  0.762  1.798
## [22231]  1.639  1.969  1.273  0.820  0.764  2.286  1.545  1.317  1.652  1.032
## [22241]  1.025  1.609  1.419  1.202  1.556  1.224  1.320  1.372  1.235  1.312
## [22251]  1.385  1.361  1.240  1.220  1.047  1.276  0.938  1.115  0.819  0.974
## [22261]  0.852  0.809  1.353  1.047  1.769  1.037  1.388  1.204  1.385  1.217
## [22271]  1.325  1.480  1.363  1.205  1.214  1.068  1.127  1.119  1.487  1.195
## [22281]  1.326  2.230  1.216  1.947  1.274  1.242  2.661  1.053  1.731  1.006
## [22291]  1.310  0.832  1.312  1.121  1.133  1.061  1.260  3.824  2.356  3.138
## [22301]  1.570  0.761  1.922  1.802  1.903  2.543  2.018  1.419  2.163  1.358
## [22311]  2.507  1.373  3.497  1.185  1.775  1.499  1.695  1.320  1.497  3.223
## [22321]  1.708  1.482  1.584  0.981  1.815  1.280  1.262  1.195  1.629  1.667
## [22331]  1.850  1.311  2.221  1.473  2.281  1.585  1.937  1.903  1.816  2.011
## [22341]  1.570  1.525  1.626  2.030  2.222  2.025  1.451  1.655  1.620  1.811
## [22351]  1.371  1.828  1.625  2.229  1.459  1.804  1.342  1.567  1.767  1.910
## [22361]  1.253  1.631  2.058  2.415  2.496  2.582  2.549  3.221  3.198  2.659
## [22371]  2.393  6.273  0.884  2.560  2.291  1.259  3.591  1.812  1.520  1.245
## [22381]  1.408  1.345  1.548  1.873  2.307  1.937  1.594  1.934  0.768  0.780
## [22391]  0.796  0.771  0.773  0.832  0.775  0.793  0.793  0.818  1.284  1.080
## [22401]  0.853  0.797  0.771  0.812  0.792  0.768  2.646  0.764  0.789  0.769
## [22411]  3.347  0.761  0.761  0.763  0.913  0.761  0.761  0.762  0.878  0.930
## [22421]  0.878  0.904  0.938  0.854  0.895  0.761  0.761  0.901  0.869  0.907
## [22431]  0.944  1.015  1.062  1.211  0.907  1.055  0.927  0.862  0.801  0.772
## [22441]  0.772  1.141  2.052  1.327  1.064  1.251  0.766  0.827  0.761  0.762
## [22451]  0.760  2.665  3.201  2.609  1.484  1.464  2.043  1.729  2.165  2.171
## [22461]  1.141  1.074  1.231  0.968  2.022  2.569  1.199  2.163  1.209  0.986
## [22471]  1.159  2.233  2.049  1.927  1.375  1.560  2.208  1.339  4.605  1.439
## [22481]  1.120  3.304  2.901  1.943  1.630  1.489  2.601  1.295  1.725  1.722
## [22491]  1.785  2.368  1.868  1.323  1.362  1.264  2.747  1.675  1.329  1.632
## [22501]  0.761  0.761  0.761  1.167  1.943  2.052  1.395  0.874  1.154  1.700
## [22511]  2.483  2.226  1.354  0.889  1.167  0.941  1.527  1.585  0.929  1.610
## [22521]  1.404  1.294  1.506  1.701  7.170  1.222  1.182  2.647  3.872  5.113
## [22531]  2.936  4.079  1.434  1.158  0.875  1.524  0.943  1.330  0.919  1.030
## [22541]  2.179  1.704  1.172  1.351  0.960  2.477  1.868  2.635  0.761  0.761
## [22551]  0.761  0.762  0.761  0.761  0.761  0.761  1.257  1.243  1.318  1.124
## [22561]  1.224  0.940  0.762  0.761  1.300  1.407  1.817  1.688  1.554  1.488
## [22571]  0.929  1.016  1.065  1.289  1.486  1.477  1.004  1.960  1.107  0.776
## [22581]  2.145  1.354  1.625  0.762  1.211  1.206  2.212  2.686  1.407  2.038
## [22591]  1.256  1.239  1.133  0.761  1.048  1.677  1.146  0.764  1.274  0.903
## [22601]  1.073  1.060  1.276  0.875  1.372  0.762  1.291  1.245  1.372  1.819
## [22611]  0.990  1.383  1.400  0.933  1.184  1.178  1.230  0.814  1.016  0.803
## [22621]  0.799  0.914  1.059  1.200  1.418  1.663  1.644  1.790  1.085  1.604
## [22631]  1.350  1.638  1.123  1.543  1.185  1.434  1.544  1.416  1.148  2.137
## [22641]  2.010  1.326  2.391  2.464  1.999  1.863  1.625  1.840  2.308  1.849
## [22651]  1.206  1.309  1.041  1.312  0.966  1.739  1.033  1.572  1.662  1.378
## [22661]  0.786  1.042  0.969  1.073  0.895  1.142  1.165  1.464  1.100  0.800
## [22671]  1.437  1.410  0.897  2.171  1.701  1.303  0.863  0.894  1.306  3.566
## [22681]  1.301  0.766  0.842  1.082  1.069  1.273  0.808  1.528  5.018  0.761
## [22691]  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [22701]  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  1.615
## [22711]  2.881  0.761  0.763  0.761  0.761  0.761  0.766  0.763  0.761  0.761
## [22721]  0.761  0.761  0.761  2.006  0.761  0.768  0.791  0.842  1.543  1.873
## [22731]  1.404  1.584  1.869  1.551  1.422  1.890  1.374  0.993  1.864  2.140
## [22741]  1.684  2.259  1.885  2.351  2.108  2.487  1.692  2.155  1.258  2.126
## [22751]  1.061  3.545  3.784  2.508  1.966  1.736  1.164  1.480  1.662  1.645
## [22761]  1.695  1.880  1.833  1.907  2.437  2.673  2.132  1.843  2.303  2.015
## [22771]  2.051  2.386  1.955  1.895  1.676  1.838  1.832  1.681  0.955  2.230
## [22781]  1.637  2.149  2.604  2.135  1.092  2.264  1.188  1.789  1.297  1.564
## [22791]  1.743  1.635  1.800  1.553  1.822  1.583  1.768  1.494  1.423  1.892
## [22801]  1.367  1.560  1.389  1.563  1.863  2.135  1.551  1.852  1.513  1.532
## [22811]  2.282  1.900  1.922  2.072  2.015  1.955  1.938  2.179  2.046  2.070
## [22821]  1.584  1.334  2.151  2.185  1.751  2.105  2.263  2.123  2.071  2.054
## [22831]  2.187  1.757  1.072  1.345  1.302  1.461  1.445  1.095  0.798  1.017
## [22841]  1.726  1.833  1.823  1.990  1.925  1.928  1.651  1.603  1.608  1.625
## [22851]  1.837  1.771  1.332  2.977  1.021  0.927  1.044  1.369  1.007  1.177
## [22861]  1.234  0.892  1.361  1.429  1.055  1.127  1.133  1.084  1.131  0.978
## [22871]  1.299  1.274  1.319  1.237  1.208  1.069  1.348  1.409  2.249  1.384
## [22881]  3.306  1.449  1.281  1.432  1.515  2.323  1.250  1.389  1.768  1.548
## [22891]  0.917  1.024  1.011  1.170  1.015  0.941  1.283  1.140  1.371  1.093
## [22901]  1.157  1.228  1.043  1.128  1.251  1.208  1.021  1.027  1.150  1.174
## [22911]  1.396  1.297  1.617  1.313  2.206  1.021  1.871  1.490  1.829  1.462
## [22921]  2.283  1.496  2.336  1.478  1.640  1.361  1.784  1.408  1.349  1.282
## [22931]  1.301  1.155  1.378  1.468  1.440  1.446  1.454  1.462  1.437  1.416
## [22941]  1.426  1.652  2.027  2.280  2.356  2.029  2.218  2.260  1.471  0.985
## [22951]  2.626  1.424  2.313  3.677  2.748  1.906  2.214  2.158  2.021  1.419
## [22961]  1.933  1.721  1.683  2.127  1.298  1.782  2.056  1.118  1.353  2.055
## [22971]  1.362  1.088  1.401  1.006  1.526  1.196  1.714  1.033  1.628  1.412
## [22981]  1.281  1.004  1.395  2.818  1.305  2.479  1.282  1.795  1.662  2.090
## [22991]  0.761  2.766  0.761  2.030  1.131  1.897  0.892  1.476  2.065  1.403
## [23001]  1.590  2.632  1.992  2.011  1.586  1.307  2.409  1.927  1.580  1.597
## [23011]  1.870  1.495  1.466  1.572  1.510  1.582  1.452  2.296  1.712  1.656
## [23021]  1.657  1.515  1.556  1.375  1.714  1.906  1.842  1.486  1.385  1.459
## [23031]  1.454  1.436  1.907  1.367  2.175  2.606  2.086  1.326  1.848  1.157
## [23041]  1.463  1.100  1.330  0.934  1.264  1.410  1.484  1.266  1.035  1.267
## [23051]  1.167  1.195  1.393  1.343  1.487  1.505  1.433  1.397  1.527  0.761
## [23061]  1.396  0.993  1.395  1.526  1.527  1.183  1.260  1.195  0.762  1.903
## [23071]  1.284  1.306  0.761  1.518  1.785  1.428  1.515  0.761  1.491  1.526
## [23081]  1.912  1.604  2.878  1.515  1.880  1.508  1.884  1.470  1.525  1.514
## [23091]  1.522  1.413  2.170  1.790  1.753  1.593  1.387  1.295  1.527  1.463
## [23101]  0.761  0.761  1.096  1.052  0.761  1.465  1.523  1.504  1.500  1.520
## [23111]  0.761  1.453  0.761  0.762  1.985  2.194  1.414  2.162  0.775  1.708
## [23121]  0.863  0.838  1.123  0.905  1.329  0.986  0.977  0.761  0.761  0.763
## [23131]  1.370  1.438  0.761  0.761  0.762  0.763  1.035  0.761  0.761  1.109
## [23141]  0.761  0.763  0.762  0.761  1.475  0.762  0.762  0.761  1.503  0.762
## [23151]  0.762  0.761  0.762  1.469  1.477  0.761  1.341  1.275  1.982  0.761
## [23161]  1.474  0.762  0.762  0.761  0.762  1.462  0.761  0.762  0.762  1.104
## [23171]  0.761  0.762  1.387  1.456  1.244  0.762  0.762  1.337  0.762  0.762
## [23181]  0.762  1.995  0.762  0.762  1.192  1.442  0.762  0.762  0.761  0.762
## [23191]  0.762  0.762  0.761  0.762  1.186  0.762  0.761  1.103  1.528  0.762
## [23201]  0.761  1.470  0.762  0.762  0.761  0.762  0.762  0.762  0.923  1.355
## [23211]  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762
## [23221]  0.761  0.762  0.762  0.762  0.761  0.762  0.998  0.762  0.761  0.762
## [23231]  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762
## [23241]  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762
## [23251]  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762
## [23261]  0.761  1.424  0.762  0.761  0.762  0.762  2.050  0.761  0.762  0.762
## [23271]  0.762  1.424  0.762  0.762  2.282  0.762  0.762  0.762  1.264  0.762
## [23281]  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761
## [23291]  0.762  0.762  0.761  0.762  0.762  0.762  1.347  0.762  0.762  0.762
## [23301]  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762
## [23311]  0.762  0.762  1.438  0.762  0.762  0.762  1.153  0.762  0.762  0.762
## [23321]  0.761  0.762  0.762  0.761  0.761  0.762  1.180  0.762  0.761  0.762
## [23331]  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762
## [23341]  0.761  1.260  0.762  0.762  0.761  0.762  0.762  0.761  0.762  0.761
## [23351]  0.762  0.762  0.761  0.762  0.762  0.761  0.762  0.762  0.761  0.762
## [23361]  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761
## [23371]  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.761  0.761
## [23381]  0.762  1.119  0.762  0.762  1.374  0.762  1.146  1.089  1.924  0.762
## [23391]  0.762  2.134  0.762  0.762  0.762  0.762  0.892  0.762  0.762  0.762
## [23401]  0.762  0.764  0.762  0.762  0.762  0.762  1.189  0.761  0.761  1.603
## [23411]  0.762  0.762  2.100  1.518  2.212  1.527  1.506  1.177  1.410  0.761
## [23421]  0.762  0.762  0.762  0.762  1.524  1.418  2.244  0.762  0.762  0.762
## [23431]  0.762  0.761  1.686  1.632  0.762  2.230  1.526  0.761  1.746  1.527
## [23441]  2.283  1.162  1.322  1.343  1.327  1.036  0.761  0.762  1.336  0.771
## [23451]  2.161  1.964  1.768  1.289  1.553  0.762  0.762  1.178  1.438  0.761
## [23461]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.897  1.419
## [23471]  0.761  0.761  1.521  1.397  0.761  0.761  1.140  1.449  0.762  0.761
## [23481]  1.505  1.467  0.761  1.250  1.527  0.761  0.762  1.515  1.180  0.762
## [23491]  0.762  1.511  1.267  0.762  0.762  1.419  1.514  0.762  0.761  1.490
## [23501]  1.517  0.762  0.762  1.073  1.039  0.762  0.762  0.896  0.762  0.762
## [23511]  0.762  0.761  0.899  0.761  0.762  0.823  0.761  0.761  0.762  1.321
## [23521]  0.890  0.761  0.761  1.170  1.491  0.761  0.761  0.761  1.513  0.761
## [23531]  0.762  0.761  1.517  0.762  0.762  1.095  1.524  0.761  0.762  1.520
## [23541]  1.386  0.761  0.762  1.514  1.527  0.761  0.762  1.501  1.488  0.761
## [23551]  0.762  1.526  1.453  0.761  1.411  1.099  0.761  0.761  1.657  1.453
## [23561]  0.761  0.761  1.507  0.874  0.761  0.761  1.287  1.117  0.761  0.761
## [23571]  1.463  1.090  0.761  0.761  1.893  1.472  0.761  0.761  2.134  1.525
## [23581]  0.761  0.761  0.761  1.482  0.761  0.761  1.524  1.863  0.761  0.761
## [23591]  1.527  1.293  0.761  0.761  1.298  1.517  0.761  0.761  1.305  1.318
## [23601]  0.761  0.761  1.453  0.761  0.761  1.511  0.761  0.761  0.761  0.761
## [23611]  0.761  0.761  0.761  1.431  0.761  0.761  0.761  0.761  0.761  1.936
## [23621]  0.761  0.761  0.761  1.480  0.761  1.524  0.762  0.761  0.761  1.346
## [23631]  1.137  0.761  0.761  1.475  0.761  0.761  1.092  0.761  1.482  0.869
## [23641]  0.761  0.761  0.861  0.761  0.763  0.761  0.761  1.740  0.761  0.761
## [23651]  1.192  0.842  1.322  0.868  0.761  0.761  1.109  0.858  0.761  0.761
## [23661]  1.159  1.375  0.762  0.761  0.761  0.761  0.762  0.761  0.762  0.761
## [23671]  0.762  0.761  0.762  0.762  0.762  0.762  1.103  0.762  0.762  0.762
## [23681]  0.762  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.761
## [23691]  0.762  0.761  0.761  0.761  0.777  0.761  0.762  0.978  0.762  0.762
## [23701]  0.761  1.100  0.762  0.761  0.761  0.761  0.762  1.198  0.761  0.762
## [23711]  0.761  0.762  0.938  0.762  0.761  0.761  0.761  0.762  0.761  0.761
## [23721]  1.507  0.761  0.761  1.333  0.762  0.761  0.761  0.761  0.762  0.761
## [23731]  0.761  0.761  0.762  0.761  1.453  1.357  0.761  0.762  0.761  0.761
## [23741]  0.761  0.761  0.761  0.761  0.761  0.761  1.527  0.761  0.761  0.761
## [23751]  0.761  0.761  0.761  1.431  0.761  0.761  1.936  0.761  0.761  1.527
## [23761]  1.528  0.761  0.761  0.761  0.761  0.761  0.761  1.507  1.411  0.761
## [23771]  0.761  1.523  1.619  1.511  0.761  0.762  0.761  0.761  0.761  0.761
## [23781]  0.761  1.247  0.761  0.761  0.889  0.761  0.761  0.761  0.761  0.761
## [23791]  0.761  0.761  0.761  0.880  0.761  0.761  0.761  0.761  0.761  0.761
## [23801]  0.761  1.503  0.761  1.503  1.375  1.619  1.274  0.761  1.504  1.477
## [23811]  0.987  0.761  0.761  0.762  1.655  0.761  0.761  0.762  0.762  1.487
## [23821]  0.761  0.762  0.761  0.762  1.180  1.405  0.761  0.761  0.762  1.483
## [23831]  1.416  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [23841]  0.762  0.762  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.762
## [23851]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [23861]  0.762  1.207  0.762  0.762  0.762  0.762  1.147  0.761  0.762  0.762
## [23871]  1.360  0.761  0.761  0.762  0.762  0.762  0.761  0.761  1.374  0.761
## [23881]  2.198  0.761  0.761  0.762  0.761  0.761  0.761  0.762  1.311  1.509
## [23891]  1.341  0.762  1.410  1.112  0.761  0.761  0.762  0.761  0.761  0.761
## [23901]  1.414  1.526  0.761  0.762  0.762  0.762  0.895  1.381  0.762  0.762
## [23911]  0.762  1.518  0.762  0.762  0.762  1.358  0.762  0.762  0.762  0.762
## [23921]  1.459  0.762  0.762  0.762  1.424  1.445  0.762  0.762  0.762  0.762
## [23931]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  1.137
## [23941]  0.762  0.762  0.762  1.431  1.132  0.762  0.762  0.762  2.070  0.762
## [23951]  0.762  0.762  1.511  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [23961]  0.762  1.311  1.805  0.762  0.762  1.410  0.761  0.762  0.762  0.762
## [23971]  2.981  0.762  0.762  0.762  1.523  0.762  0.762  0.762  0.762  0.762
## [23981]  0.762  0.762  0.761  1.311  0.762  0.762  0.762  1.796  0.761  0.762
## [23991]  0.762  0.762  0.762  1.142  1.317  0.762  0.762  0.762  0.761  1.407
## [24001]  0.762  0.762  0.762  0.762  0.761  1.527  0.762  0.762  0.762  0.762
## [24011]  0.762  1.481  0.762  0.762  0.762  0.762  1.310  0.762  0.829  0.762
## [24021]  0.762  1.499  0.762  0.762  0.762  0.762  1.457  0.762  0.762  0.762
## [24031]  1.438  0.762  0.762  0.762  0.762  0.761  0.761  0.762  0.762  0.762
## [24041]  0.762  0.761  0.761  1.284  0.762  0.762  0.761  0.761  0.762  0.762
## [24051]  0.762  0.761  1.424  0.762  0.761  0.761  0.762  0.762  0.761  0.762
## [24061]  0.762  0.761  1.221  1.167  0.761  1.527  0.762  1.475  0.761  1.012
## [24071]  0.762  0.762  0.762  0.762  0.761  1.502  0.762  0.762  0.987  0.762
## [24081]  0.761  1.517  0.762  0.761  0.762  0.762  0.761  0.762  0.762  0.762
## [24091]  0.761  0.762  0.761  0.762  0.761  1.063  0.761  0.762  0.761  0.762
## [24101]  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.762  0.762  0.851
## [24111]  0.762  0.761  0.996  0.761  0.762  0.761  0.762  1.949  0.761  0.762
## [24121]  0.761  0.910  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761
## [24131]  1.109  0.761  1.422  0.761  0.828  0.761  0.761  0.761  0.761  0.761
## [24141]  0.761  0.761  0.761  0.761  1.496  0.761  0.761  1.084  0.761  0.761
## [24151]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [24161]  1.123  0.761  0.761  0.839  0.761  1.287  0.762  1.252  1.025  1.507
## [24171]  0.761  0.761  1.438  1.151  1.411  1.263  1.109  0.761  0.762  0.761
## [24181]  0.762  0.761  0.761  0.762  0.761  0.762  0.762  0.762  0.761  0.762
## [24191]  0.762  0.762  0.762  0.761  0.762  0.762  0.761  0.761  0.761  0.761
## [24201]  0.762  0.761  0.761  0.761  0.761  0.762  0.762  0.762  0.762  0.761
## [24211]  0.762  0.762  0.762  0.761  0.762  0.762  0.761  0.762  1.197  0.762
## [24221]  0.761  0.761  1.444  0.761  0.761  1.374  0.761  0.762  0.761  0.762
## [24231]  0.761  0.761  0.762  0.761  0.762  0.761  0.762  0.761  0.761  0.762
## [24241]  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [24251]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [24261]  2.122  1.527  0.761  1.137  0.761  0.761  1.517  0.761  0.762  0.761
## [24271]  0.761  1.411  0.761  0.761  0.761  1.923  0.761  2.027  0.762  0.761
## [24281]  0.762  0.762  0.762  0.762  0.762  0.762  1.184  0.762  0.761  0.761
## [24291]  0.762  1.973  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [24301]  0.762  0.761  0.762  0.761  0.762  0.761  0.761  0.761  0.761  0.761
## [24311]  0.761  0.762  0.761  0.762  0.761  0.761  0.762  1.522  0.761  0.762
## [24321]  0.762  0.761  0.762  0.761  0.762  0.761  0.761  0.761  0.761  0.762
## [24331]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [24341]  0.762  0.762  0.761  1.487  0.761  1.374  1.224  1.092  1.063  1.492
## [24351]  3.191  3.424  3.158  1.598  3.116  3.439  2.335  2.040  1.572  1.234
## [24361]  0.859  0.951  1.050  1.334  1.842  1.980  1.396  1.422  1.710  0.800
## [24371]  1.660  1.581  0.912  1.470  2.270  1.712  1.388  1.674  1.374  1.622
## [24381]  3.220  1.287  2.252  2.905  2.041  2.809  3.249  0.761  3.040  0.762
## [24391]  2.005  0.761  2.242  6.419  0.764  1.384  0.981  2.480  3.608  2.917
## [24401]  7.595  2.294  4.346  2.149  2.994  2.159  5.007  1.702  4.290  5.729
## [24411]  6.758  3.881  4.552  2.657  3.757  7.285  4.808  3.424  4.868  2.899
## [24421]  3.431  3.986  6.578  2.155  2.754  1.850  2.488  2.237  2.340  2.265
## [24431]  2.243  2.335  2.407  1.828  2.474  1.890  2.221  1.607  2.110  1.351
## [24441]  2.284  2.179  2.415  2.181  2.111  2.252  1.791  1.779  2.011  1.826
## [24451]  2.093  2.073  1.781  2.205  1.675  2.050  1.803  3.258  1.579  2.664
## [24461]  2.077  2.387  1.452  1.894  1.742  4.319  3.300  1.027  1.600  1.146
## [24471]  1.763  1.148  2.872  1.146  4.088  2.790  3.351  1.686  1.925  1.258
## [24481]  1.442  4.061  1.273  9.722  6.436  7.040  7.093  6.753  7.974 11.149
## [24491]  5.494  4.417  7.775  3.139  7.882  6.483  5.957  6.503  3.114  1.377
## [24501]  1.038  1.676  2.082  1.662  2.318  1.089  2.262  2.070  2.683  2.112
## [24511]  1.988  2.609  2.168  2.096  2.234  2.241  2.334  1.862  2.165  1.979
## [24521]  1.317  1.999  1.466  1.311  2.323  1.401  2.208  1.303  2.038  1.395
## [24531]  2.108  1.231  1.986  1.103  1.304  1.551  1.393  1.862  2.244  1.681
## [24541]  1.100  0.761  0.762  0.761  0.762  3.032  1.247  3.194  5.279  4.739
## [24551]  2.027  4.129  2.561  3.269  4.189  1.468  1.269  2.681  1.993  2.791
## [24561]  3.215  4.315  5.551  5.477  4.364  2.829  3.659  3.144  8.605  2.874
## [24571]  1.698  1.654  1.384  1.006  1.268  2.364  2.821  1.674  1.478  1.579
## [24581]  2.116  1.766  1.583  1.634  1.715  1.687  1.610  1.042  0.761  0.762
## [24591]  0.761  0.762  0.761  0.762  1.697  7.618  1.218  4.648  8.875  7.401
## [24601]  7.650  3.922  5.944  1.640  1.295  1.684  1.587  1.788  1.459  2.072
## [24611]  2.242  2.465  1.566  2.078  1.872  2.071  1.988  1.831  2.185  0.762
## [24621]  0.761  0.764  0.761  0.761  0.761  0.761  0.761  0.761  0.761  1.571
## [24631]  0.767  0.763  0.761  0.762  0.762  2.166  1.864  1.422  1.613  1.715
## [24641]  1.022  1.887  2.045  2.290  2.097  2.167  1.959  2.725  2.360  1.607
## [24651]  0.975  1.316  1.182  1.247  2.683  1.432  7.266  1.521  2.602  1.035
## [24661]  2.552  1.350  9.255  3.083  6.517  5.857  4.672  8.100  1.992  2.044
## [24671]  4.335  2.551  0.953  1.719  1.327  1.498  0.958  1.054  3.069  1.220
## [24681]  1.707  3.020  2.032  2.029  1.286  0.958  1.488  1.145  1.303  1.423
## [24691]  1.258  1.280  1.226  0.787  1.091  1.541 11.975  3.568  2.704  2.313
## [24701]  9.652  2.231  4.648  5.858  6.964  4.225  3.395  1.601  1.447  2.763
## [24711]  2.698  1.701  1.150  0.761  0.762  0.761  0.762  0.906  0.762  0.942
## [24721]  1.081  1.572  5.374  1.648  1.684  1.385  2.130  4.621  3.821  5.233
## [24731]  6.187  6.514  4.849  4.905  3.598  5.273  5.458  4.593  6.036  1.658
## [24741]  2.021  1.518  1.432  1.406  1.489  1.780  1.826  1.373  1.685  1.751
## [24751]  1.870  1.888  2.182  1.909  1.654  1.909  1.512  1.752  1.773  1.219
## [24761]  1.786  1.599  1.624  1.348  1.242  2.105  1.723  1.160  1.434  2.827
## [24771]  1.432  2.746  1.380  1.306  2.255  1.131  0.761  0.762  0.761  0.762
## [24781]  0.761  0.762  0.765  1.163  3.480  1.520  5.813  8.022  6.582  5.747
## [24791]  6.456  6.995  6.768  7.067  6.423  4.862  5.624  5.636  6.363  7.289
## [24801]  3.677  2.475  1.455  2.521  1.344  1.324  1.387  1.302  1.457  2.289
## [24811]  1.684  1.723  1.390  1.416  1.844  2.075  1.967  2.161  1.554  1.750
## [24821]  1.868  1.428  1.504  1.294  1.524  1.291  1.726  1.265  1.920  1.779
## [24831]  2.614  1.390  1.731  1.947  1.655  2.101  1.453 10.786  6.310  6.884
## [24841]  5.578  6.710  7.002  3.200  3.147  5.500  2.516  3.878  4.383  6.587
## [24851]  7.341  2.817  5.978  1.048  3.365  1.382  2.060  1.324  1.615  1.994
## [24861]  1.396  1.870  1.801  1.352  1.654  3.879  2.038  1.388  1.798  1.292
## [24871]  2.265  1.424  1.171  1.646  1.462  1.501  1.555  1.374  1.388  0.916
## [24881]  0.910  0.775  0.761  0.761  0.761  0.760  0.761  0.761  0.761  1.652
## [24891]  0.761  2.099  2.149  1.954  2.059  1.973  2.024  1.862  0.889  0.762
## [24901]  0.761  0.764  0.785  0.798  0.762  0.761  0.767  0.761  0.826  0.816
## [24911]  0.834  1.380  0.977  1.327  1.476  1.716  1.336  1.398  1.331  1.694
## [24921]  1.141  1.885  2.088  1.295  2.055  1.922  2.003  1.917  6.133  1.294
## [24931]  1.869  1.772  1.944  1.858  1.910  1.993  1.758  1.492  1.906  1.147
## [24941]  0.761  0.762  0.772  0.761  0.766  0.763  0.765  0.765  0.761  0.761
## [24951]  0.761  0.761  0.761  0.803  0.786  0.773  0.761  0.948  0.922  0.963
## [24961]  1.288  1.767  1.270  1.740  1.307  1.366  1.152  1.126  1.161  1.057
## [24971]  1.038  1.264  1.332  1.146  1.314  1.235  1.328  1.295  1.548  1.313
## [24981]  1.135  1.161  1.397  1.236  1.477  1.443  0.761  0.761  0.761  0.761
## [24991]  0.761  0.762  0.761  0.766  0.766  0.778  0.931  1.466  1.276  1.274
## [25001]  1.649  1.594  1.919  1.885  1.948  1.161  2.038  2.014  2.018  2.083
## [25011]  1.405  0.761  0.762  0.766  0.839  0.779  0.898  0.769  0.787  0.844
## [25021]  0.800  0.766  0.762  0.830  0.880  0.764  0.761  0.762  0.763  0.766
## [25031]  0.779  0.764  0.762  0.761  0.871  0.816  0.761  0.764  0.761  0.763
## [25041]  0.870  1.034  0.783  0.870  1.738  0.763  1.222  0.763  0.876  0.763
## [25051]  0.858  0.763  1.000  0.763  0.867  0.765  0.763  1.116  0.763  1.153
## [25061]  0.763  0.789  0.853  0.782  0.776  0.949  0.851  0.761  0.768  0.761
## [25071]  0.764  0.762  0.763  0.762  0.763  0.761  0.763  0.762  0.763  0.762
## [25081]  0.763  0.762  0.763  0.761  0.763  0.761  0.763  0.761  0.761  0.761
## [25091]  0.761  0.761  0.777  0.767  0.761  0.766  0.761  0.761  0.761  0.761
## [25101]  0.761  0.761  0.762  0.761  0.766  0.764  0.766  0.799  1.112  1.257
## [25111]  1.141  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.763
## [25121]  0.761  0.763  0.761  0.763  0.761  0.761  0.763  0.761  0.766  0.761
## [25131]  0.763  1.220  2.004  1.548  1.125  1.856  1.601  1.807  1.193  1.292
## [25141]  2.059  4.520  1.784  1.503  1.435  1.235  2.067  1.776  1.297  0.951
## [25151]  2.109  1.969  1.828  1.311  1.951  1.410  1.397  1.793  1.091  1.057
## [25161]  1.252  1.531  1.493  1.539  1.527  1.523  1.519  1.430  1.450  0.780
## [25171]  1.102  0.830  0.802  0.846  1.018  0.910  0.907  0.899  0.913  0.931
## [25181]  1.329  0.861  0.903  1.127  1.282  1.281  1.155  0.762  0.762  0.763
## [25191]  0.881  0.767  0.882  0.772  0.892  0.768  0.889  0.767  0.902  0.763
## [25201]  0.883  0.767  0.813  0.761  0.761  0.762  0.769  0.768  0.806  0.913
## [25211]  0.822  0.775  0.816  0.824  0.837  0.881  0.924  1.102  0.894  0.912
## [25221]  0.840  0.860  0.919  0.771  1.062  1.196  1.039  0.826  0.848  0.878
## [25231]  1.327  1.366  1.151  1.319  1.406  1.348  1.430  1.292  1.454  1.052
## [25241]  1.425  1.023  1.512  1.481  2.090  0.826  1.181  1.188  1.230  1.249
## [25251]  1.361  1.194  1.623  1.430  1.818  1.365  1.492  1.675  1.711  1.637
## [25261]  1.556  1.361  1.329  1.498  1.503  1.474  0.914  1.001  0.885  0.889
## [25271]  0.960  0.945  0.963  0.941  0.902  0.977  0.950  0.965  0.919  1.162
## [25281]  1.189  1.016  0.945  0.990  1.327  2.822  1.159  1.013  1.321  1.952
## [25291]  1.868  0.896  1.783  0.935  0.880  1.060  1.139  0.931  0.903  0.887
## [25301]  0.895  0.851  0.835  0.837  0.858  0.948  0.836  0.824  0.916  0.886
## [25311]  0.949  0.941  0.894  0.831  0.901  0.891  0.836  0.929  0.908  1.014
## [25321]  0.773  0.990  0.843  0.924  0.873  0.891  0.906  0.928  0.837  0.937
## [25331]  0.881  0.951  0.902  0.903  1.236  1.169  1.061  0.828  1.264  1.268
## [25341]  1.246  1.118  0.972  1.051  0.865  1.235  1.215  1.250  1.283  1.148
## [25351]  1.138  1.182  1.187  1.231  1.119  0.768  1.173  0.761  1.163  0.761
## [25361]  1.199  0.795  0.761  0.793  0.761  0.779  0.761  0.763  0.762  0.777
## [25371]  0.761  0.762  0.764  0.776  0.761  0.761  0.761  0.761  0.761  0.761
## [25381]  0.827  0.761  0.761  1.283  0.761  0.974  0.764  0.761  0.761  1.097
## [25391]  0.761  0.762  0.761  0.761  0.840  0.840  0.775  0.837  0.767  0.879
## [25401]  0.804  0.878  1.055  0.933  1.108  2.027  0.865  0.918  1.092  0.902
## [25411]  0.873  0.842  0.774  0.771  0.826  0.765  0.777  0.796  0.785  0.799
## [25421]  0.784  0.845  0.768  0.860  0.780  0.861  0.788  0.869  0.866  0.845
## [25431]  0.908  0.944  0.904  0.888  0.821  0.880  0.906  0.819  0.869  0.872
## [25441]  0.844  0.932  0.891  0.784  0.794  0.804  0.840  0.920  1.010  0.980
## [25451]  0.811  0.766  0.888  0.762  0.931  0.877  0.871  0.776  0.871  0.853
## [25461]  1.032  0.945  0.864  1.012  0.929  0.932  0.900  0.761  0.761  0.761
## [25471]  0.761  1.841  1.842  1.411  1.700  1.652  2.004  1.569  1.360  0.974
## [25481]  1.600  1.983  1.928  0.871  1.367  1.417  1.443  1.640  1.518  0.838
## [25491]  0.764  0.769  0.887  0.773  0.785  0.773  0.775  0.768  1.490  0.765
## [25501]  0.770  0.762  0.786  0.764  0.782  0.761  0.773  0.763  0.761  0.763
## [25511]  0.798  1.706  0.762  0.785  0.765  0.778  0.814  0.889  0.816  0.761
## [25521]  0.802  1.105  0.761  0.762  1.388  1.882  0.763  0.766  0.769  1.566
## [25531]  0.761  0.762  0.763  0.762  0.762  0.761  0.761  0.762  0.761  0.761
## [25541]  0.761  0.761  0.763  0.761  0.764  0.761  0.762  0.837  0.774  1.688
## [25551]  1.370  1.729  1.936  2.181  1.330  1.000  0.762  0.763  0.762  0.762
## [25561]  0.761  0.762  0.761  0.761  0.763  0.762  0.762  1.239  0.833  1.252
## [25571]  0.761  0.761  1.274  0.901  0.779  0.771  0.769  0.779  0.782  0.791
## [25581]  0.834  0.829  0.818  0.810  0.942  0.825  0.828  0.860  0.981  1.026
## [25591]  0.872  0.861  0.846  0.765  1.463  0.764  0.763  0.763  0.766  0.763
## [25601]  1.435  0.884  0.831  0.817  0.848  0.789  0.779  0.775  0.787  0.891
## [25611]  0.787  0.794
getFitted(Total7)  #predictions of the model for all points
x = getSimulations(Total7, nsim = 5, type = "refit")  #extract simulations from the model
getRefit(Total7, x[[1]])  #model with simulated data
## Formula:          
## KUD95 ~ LengthStd + Habitat + ComImport + Spawn + MonitArea_km2 +  
##     (1 | Transmitter) + (1 | Species)
## Data: newData
##      AIC      BIC   logLik df.resid 
## 14747.76 14837.42 -7362.88    25601 
## Random-effects (co)variances:
## 
## Conditional model:
##  Groups      Name        Std.Dev.
##  Transmitter (Intercept) 0.2863  
##  Species     (Intercept) 0.1619  
## 
## Number of obs: 25612 / Conditional model: Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0736 
## 
## Fixed Effects:
## 
## Conditional model:
##            (Intercept)               LengthStd         Habitatdemersal  
##               -0.19564                 0.10132                -0.02346  
## Habitatpelagic-neritic         ComImportmedium          ComImportminor  
##                0.52188                -0.07532                -0.07252  
##               Spawnyes           MonitArea_km2  
##                0.06377                 0.03010
getRefit(Total7, getObservedResponse(Total7))  #model with real data
## Formula:          
## KUD95 ~ LengthStd + Habitat + ComImport + Spawn + MonitArea_km2 +  
##     (1 | Transmitter) + (1 | Species)
## Data: newData
##       AIC       BIC    logLik  df.resid 
##  9719.337  9808.996 -4848.669     25601 
## Random-effects (co)variances:
## 
## Conditional model:
##  Groups      Name        Std.Dev.
##  Transmitter (Intercept) 0.2887  
##  Species     (Intercept) 0.1609  
## 
## Number of obs: 25612 / Conditional model: Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0746 
## 
## Fixed Effects:
## 
## Conditional model:
##            (Intercept)               LengthStd         Habitatdemersal  
##               -0.17711                 0.21419                -0.11889  
## Habitatpelagic-neritic         ComImportmedium          ComImportminor  
##                0.51384                -0.16164                -0.11323  
##               Spawnyes           MonitArea_km2  
##                0.05662                 0.02851
#create a dataframe with the simulated data and the true data
df <- data.frame(x$sim_1, x$sim_2, x$sim_3, week_kuds$KUD95, week_kuds$LengthStd, week_kuds$Habitat, week_kuds$ComImport, week_kuds$Spawn)

#plot KUD95 (real and simulated) against Length Std
grid.arrange(ggplot(data= df, aes(x = week_kuds.LengthStd, y=week_kuds.KUD95)) + geom_point(col="black") +  scale_y_continuous(limits = c(0, 15)) + xlab("Length Std") + ylab("real KUD95"), ggplot(data= df, aes(x = week_kuds.LengthStd, y=x.sim_1)) + geom_point(col="lightblue4") +  scale_y_continuous(limits = c(0, 15)) + xlab("Length Std") + ylab("KUD95 simulation 1"), ggplot(data= df, aes(x = week_kuds.LengthStd, y=x.sim_2)) + geom_point(col="deepskyblue3") +  scale_y_continuous(limits = c(0, 15)) + xlab("Length Std") + ylab("KUD95 simulation 2"), ggplot(data= df, aes(x = week_kuds.LengthStd, y=x.sim_3)) + geom_point(col="deepskyblue4") +  scale_y_continuous(limits = c(0, 15)) + xlab("Length Std") + ylab("KUD95 simulation 3"))

#plot KUD95 (real and simulated) against Commercial Importance
grid.arrange(ggplot(data = df, aes(x = week_kuds.ComImport, y=week_kuds.KUD95)) +
  geom_boxplot(fill = "black") + scale_y_continuous(limits = c(0, 15)) + xlab("Commercial Importance") + ylab("real KUD95"), ggplot(data = df, aes(x = week_kuds.ComImport, y=x.sim_1)) +
  geom_boxplot(fill = "lightblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Commercial Importance") + ylab("KUD95 simulation 1"), ggplot(data = df, aes(x = week_kuds.ComImport, y=x.sim_2)) +
  geom_boxplot(fill = "deepskyblue3") + scale_y_continuous(limits = c(0, 15)) + xlab("Commercial Importance") + ylab("KUD95 simulation 2"), ggplot(data = df, aes(x = week_kuds.ComImport, y=x.sim_3)) + geom_boxplot(fill = "deepskyblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Commercial Importance") + ylab("KUD95 simulation 3"), ncol = 4)

#plot KUD95 (real and simulated) against Habitat
grid.arrange(ggplot(data = df, aes(x = week_kuds.Habitat, y=week_kuds.KUD95)) +
  geom_boxplot(fill = "black") + scale_y_continuous(limits = c(0, 15)) + xlab("Habitat") + ylab("real KUD95"), ggplot(data = df, aes(x = week_kuds.Habitat, y=x.sim_1)) +
  geom_boxplot(fill = "lightblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Habitat") + ylab("KUD95 simulation 1"), ggplot(data = df, aes(x = week_kuds.Habitat, y=x.sim_2)) +
  geom_boxplot(fill = "deepskyblue3") + scale_y_continuous(limits = c(0, 15)) + xlab("Habitat") + ylab("KUD95 simulation 2"), ggplot(data = df, aes(x = week_kuds.Habitat, y=x.sim_3)) + geom_boxplot(fill = "deepskyblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Habitat") + ylab("KUD95 simulation 3"), ncol = 4)

#plot KUD95 (real and simulated) against Spawn
grid.arrange(ggplot(data = df, aes(x = week_kuds.Spawn, y=week_kuds.KUD95)) +
  geom_boxplot(fill = "black") + scale_y_continuous(limits = c(0, 15)) + xlab("Spawn") + ylab("real KUD95"), ggplot(data = df, aes(x = week_kuds.Spawn, y=x.sim_1)) +
  geom_boxplot(fill = "lightblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Spawn") + ylab("KUD95 simulation 1"), ggplot(data = df, aes(x = week_kuds.Spawn, y=x.sim_2)) +
  geom_boxplot(fill = "deepskyblue3") + scale_y_continuous(limits = c(0, 15)) + xlab("Spawn") + ylab("KUD95 simulation 2"), ggplot(data = df, aes(x = week_kuds.Spawn, y=x.sim_3)) + geom_boxplot(fill = "deepskyblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Spawn") + ylab("KUD95 simulation 3"), ncol = 4)

#Backward elimination KUD50

Total1.1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1.1)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability +  
##     Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity +  
##     MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76432.6 -76294.1  38233.3 -76466.6    25595 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07234  0.2690  
##  Species     (Intercept) 0.01645  0.1282  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0602 
## 
## Conditional model:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.7081419  0.3235559  -5.279 1.30e-07 ***
## LengthStd               0.2478886  0.1440548   1.721   0.0853 .  
## BodyMassStd            -0.0198336  0.1042508  -0.190   0.8491    
## Longevity              -0.0022268  0.0031218  -0.713   0.4757    
## Vulnerability          -0.0052720  0.0038478  -1.370   0.1706    
## Troph                   0.0827987  0.0987589   0.838   0.4018    
## Habitatdemersal        -0.0813752  0.0906045  -0.898   0.3691    
## Habitatpelagic-neritic  0.2994431  0.1416864   2.113   0.0346 *  
## Migrationoceanodromous  0.0885417  0.1132002   0.782   0.4341    
## ComImportmedium        -0.1426140  0.0651104  -2.190   0.0285 *  
## ComImportminor         -0.2449377  0.1038701  -2.358   0.0184 *  
## Spawnyes                0.0470604  0.0034120  13.793  < 2e-16 ***
## ReceiverDensity         0.0002449  0.0005709   0.429   0.6679    
## MonitArea_km2           0.0227375  0.0033966   6.694 2.17e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total1.2 <- glmmTMB(KUD50 ~ LengthStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1.2)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ LengthStd + Longevity + Vulnerability + Troph + Habitat +  
##     Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 +  
##     (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76434.6 -76304.2  38233.3 -76466.6    25596 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07233  0.2689  
##  Species     (Intercept) 0.01662  0.1289  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0602 
## 
## Conditional model:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.7113483  0.3243349  -5.276 1.32e-07 ***
## LengthStd               0.2281344  0.0998303   2.285   0.0223 *  
## Longevity              -0.0022325  0.0031354  -0.712   0.4764    
## Vulnerability          -0.0051957  0.0038408  -1.353   0.1761    
## Troph                   0.0833887  0.0990848   0.842   0.4000    
## Habitatdemersal        -0.0778697  0.0890151  -0.875   0.3817    
## Habitatpelagic-neritic  0.3004002  0.1421469   2.113   0.0346 *  
## Migrationoceanodromous  0.0898653  0.1134621   0.792   0.4283    
## ComImportmedium        -0.1444110  0.0646329  -2.234   0.0255 *  
## ComImportminor         -0.2480880  0.1028815  -2.411   0.0159 *  
## Spawnyes                0.0470596  0.0034120  13.792  < 2e-16 ***
## ReceiverDensity         0.0002542  0.0005695   0.446   0.6554    
## MonitArea_km2           0.0228464  0.0033516   6.817 9.32e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total1.3 <- glmmTMB(KUD50 ~ LengthStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1.3)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ LengthStd + Longevity + Vulnerability + Troph + Habitat +  
##     Migration + ComImport + Spawn + MonitArea_km2 + (1 | Transmitter) +  
##     (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76436.4 -76314.1  38233.2 -76466.4    25597 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07238  0.2690  
##  Species     (Intercept) 0.01673  0.1293  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0602 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.715989   0.324945  -5.281 1.29e-07 ***
## LengthStd               0.227107   0.099756   2.277   0.0228 *  
## Longevity              -0.002222   0.003144  -0.707   0.4797    
## Vulnerability          -0.005308   0.003843  -1.381   0.1672    
## Troph                   0.089052   0.098521   0.904   0.3661    
## Habitatdemersal        -0.077739   0.089235  -0.871   0.3837    
## Habitatpelagic-neritic  0.290814   0.140846   2.065   0.0389 *  
## Migrationoceanodromous  0.094281   0.113322   0.832   0.4054    
## ComImportmedium        -0.142197   0.064611  -2.201   0.0277 *  
## ComImportminor         -0.246611   0.103088  -2.392   0.0167 *  
## Spawnyes                0.047055   0.003412  13.791  < 2e-16 ***
## MonitArea_km2           0.021927   0.002648   8.281  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total1.4 <- glmmTMB(KUD50 ~ LengthStd + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1.4)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ LengthStd + Vulnerability + Troph + Habitat + Migration +  
##     ComImport + Spawn + MonitArea_km2 + (1 | Transmitter) + (1 |      Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76437.9 -76323.8  38232.9 -76465.9    25598 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07239  0.2690  
##  Species     (Intercept) 0.01718  0.1311  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0602 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.665073   0.320013  -5.203 1.96e-07 ***
## LengthStd               0.232445   0.099487   2.336   0.0195 *  
## Vulnerability          -0.006324   0.003581  -1.766   0.0774 .  
## Troph                   0.077523   0.098291   0.789   0.4303    
## Habitatdemersal        -0.079825   0.089974  -0.887   0.3750    
## Habitatpelagic-neritic  0.338989   0.125689   2.697   0.0070 ** 
## Migrationoceanodromous  0.086845   0.114165   0.761   0.4468    
## ComImportmedium        -0.143100   0.065299  -2.191   0.0284 *  
## ComImportminor         -0.244772   0.104039  -2.353   0.0186 *  
## Spawnyes                0.047069   0.003412  13.795  < 2e-16 ***
## MonitArea_km2           0.021706   0.002631   8.249  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total1.5 <- glmmTMB(KUD50 ~ LengthStd + Vulnerability + Troph + Habitat + ComImport + Spawn + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1.5)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ LengthStd + Vulnerability + Troph + Habitat + ComImport +  
##     Spawn + MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76439.3 -76333.4  38232.7 -76465.3    25599 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07235  0.2690  
##  Species     (Intercept) 0.01812  0.1346  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0602 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.607821   0.317058  -5.071 3.96e-07 ***
## LengthStd               0.226063   0.099395   2.274 0.022943 *  
## Vulnerability          -0.005052   0.003246  -1.556 0.119691    
## Troph                   0.054631   0.095628   0.571 0.567806    
## Habitatdemersal        -0.120873   0.073614  -1.642 0.100593    
## Habitatpelagic-neritic  0.390057   0.109510   3.562 0.000368 ***
## ComImportmedium        -0.140886   0.066606  -2.115 0.034413 *  
## ComImportminor         -0.227813   0.103578  -2.199 0.027847 *  
## Spawnyes                0.047084   0.003412  13.800  < 2e-16 ***
## MonitArea_km2           0.021633   0.002635   8.209 2.23e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total1.6 <- glmmTMB(KUD50 ~ LengthStd + Vulnerability + Habitat + ComImport + Spawn + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1.6)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ LengthStd + Vulnerability + Habitat + ComImport + Spawn +  
##     MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76441.0 -76343.2  38232.5 -76465.0    25600 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07229  0.2689  
##  Species     (Intercept) 0.01864  0.1365  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0602 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.461890   0.187438  -7.799 6.22e-15 ***
## LengthStd               0.223358   0.099392   2.247   0.0246 *  
## Vulnerability          -0.004030   0.002749  -1.466   0.1427    
## Habitatdemersal        -0.124167   0.074185  -1.674   0.0942 .  
## Habitatpelagic-neritic  0.420717   0.097150   4.331 1.49e-05 ***
## ComImportmedium        -0.138794   0.067259  -2.064   0.0391 *  
## ComImportminor         -0.213046   0.101376  -2.102   0.0356 *  
## Spawnyes                0.047092   0.003412  13.803  < 2e-16 ***
## MonitArea_km2           0.021685   0.002637   8.224  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total1.7 <- glmmTMB(KUD50 ~ LengthStd + Habitat + ComImport + Spawn + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1.7)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ LengthStd + Habitat + ComImport + Spawn + MonitArea_km2 +  
##     (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76441.0 -76351.4  38231.5 -76463.0    25601 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07213  0.2686  
##  Species     (Intercept) 0.02152  0.1467  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0602 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.714496   0.077300 -22.180  < 2e-16 ***
## LengthStd               0.223471   0.099994   2.235   0.0254 *  
## Habitatdemersal        -0.104136   0.076803  -1.356   0.1751    
## Habitatpelagic-neritic  0.403986   0.101217   3.991 6.57e-05 ***
## ComImportmedium        -0.137607   0.071240  -1.932   0.0534 .  
## ComImportminor         -0.144917   0.094705  -1.530   0.1260    
## Spawnyes                0.047077   0.003412  13.798  < 2e-16 ***
## MonitArea_km2           0.022276   0.002625   8.486  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total1.8 <- glmmTMB(KUD50 ~ LengthStd + Habitat + Spawn + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1.8)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ LengthStd + Habitat + Spawn + MonitArea_km2 + (1 | Transmitter) +  
##     (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76440.8 -76367.4  38229.4 -76458.8    25603 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07208  0.2685  
##  Species     (Intercept) 0.02666  0.1633  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0602 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.780871   0.075743 -23.512  < 2e-16 ***
## LengthStd               0.225324   0.099873   2.256 0.024064 *  
## Habitatdemersal        -0.129265   0.078568  -1.645 0.099915 .  
## Habitatpelagic-neritic  0.369453   0.107199   3.446 0.000568 ***
## Spawnyes                0.047054   0.003412  13.791  < 2e-16 ***
## MonitArea_km2           0.022279   0.002656   8.389  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Exploratory analysis
plot(week_kuds$Week, week_kuds$KUD95)

glm_week <- glm(KUD95 ~ Week, data = week_kuds, family=Gamma(link="log"))
with(week_kuds, plot(KUD95 ~ Week, pch = 1, col="deepskyblue"))
seq <- levels(week_kuds$Week)
predictweek <- predict(glm_week,newdata=data.frame(Week=seq), type="response")
lines(seq, predictweek, lty=1, col="red")
## Warning in xy.coords(x, y): NAs introduced by coercion

week_kuds$Week <- as.numeric(week_kuds$Week)

##See how KUD varies over Weeks by Spawning season
week_kuds_ss <- subset(week_kuds, SpawnSeason == "SS")
week_kuds_a <- subset(week_kuds, SpawnSeason == "A")
week_kuds_w <- subset(week_kuds, SpawnSeason == "W")

grid.arrange(
ggplot(week_kuds_ss, aes(x = Week, y = KUD95)) +
  geom_point(col = "green") +
  labs(title = "KUD95 over Weeks SS",
       x = "Week",
       y = "KUD95") +
  scale_y_continuous(limits = c(0, 15)) +
  theme_minimal() ,


ggplot(week_kuds_a, aes(x = Week, y = KUD95)) +
  geom_point(col = "red") +
  labs(title = "KUD95 over Weeks A",
       x = "Week",
       y = "KUD95") +
  scale_y_continuous(limits = c(0, 15)) +
  theme_minimal() ,

ggplot(week_kuds_w, aes(x = Week, y = KUD95)) +
  geom_point(col = "blue") +
  labs(title = "KUD95 over Weeks W",
       x = "Week",
       y = "KUD95") +
  scale_y_continuous(limits = c(0, 15)) +
  theme_minimal()
)

#With predictions
#SS
gam_ss <- gam(KUD95 ~ s(Week), data = week_kuds_ss[week_kuds_ss$SpawnSeason == "SS", ], family = Gamma(link = "log"))
summary(gam_ss)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## KUD95 ~ s(Week)
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.065008   0.004294   15.14   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##           edf Ref.df    F p-value    
## s(Week) 7.774  8.451 14.3  <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.0049   Deviance explained =  1.2%
## GCV = 0.18719  Scale est. = 0.41752   n = 22642
week_kuds_ss$predicted <- predict(gam_ss, newdata = week_kuds_ss, type = "response")

plotss<- ggplot(week_kuds_ss, aes(x = Week, y = KUD95)) +
  geom_point(col = "green") +
  geom_line(aes(y = predicted), col = "black", linetype = "dashed") +
  labs(title = "KUD95 over Weeks (SpawnSeason = SS)",
       x = "Week",
       y = "KUD95") +
  scale_y_continuous(limits = c(0, 15)) +
  theme_minimal()

#SA
gam_a <- gam(KUD95 ~ s(Week), data = week_kuds_a[week_kuds_a$SpawnSeason == "A", ], family = Gamma(link = "log"))
summary(gam_a)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## KUD95 ~ s(Week)
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.37619    0.01799   20.91   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##           edf Ref.df     F p-value
## s(Week) 4.532  5.558 1.304   0.252
## 
## R-sq.(adj) =  0.00297   Deviance explained =  1.1%
## GCV = 0.26384  Scale est. = 0.43226   n = 1335
week_kuds_a$predicted <- predict(gam_a, newdata = week_kuds_a, type = "response")

plota <- ggplot(week_kuds_a, aes(x = Week, y = KUD95)) +
  geom_point(col = "red") +
  geom_line(aes(y = predicted), col = "black", linetype = "dashed") +
  labs(title = "KUD95 over Weeks (SpawnSeason = A)",
       x = "Week",
       y = "KUD95") +
  scale_y_continuous(limits = c(0, 15)) +
  theme_minimal()

#W
gam_w <- gam(KUD95 ~ s(Week), data = week_kuds_w[week_kuds_w$SpawnSeason == "W", ], family = Gamma(link = "log"))
summary(gam_w)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## KUD95 ~ s(Week)
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.43817    0.01598   27.42   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##          edf Ref.df     F p-value    
## s(Week) 8.48  8.923 34.47  <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.131   Deviance explained =   21%
## GCV = 0.31676  Scale est. = 0.4175    n = 1635
week_kuds_w$predicted <- predict(gam_a, newdata = week_kuds_w, type = "response")

plotw <- ggplot(week_kuds_w, aes(x = Week, y = KUD95)) +
  geom_point(col = "blue") +
  geom_line(aes(y = predicted), col = "black", linetype = "dashed") +
  labs(title = "KUD95 over Weeks (SpawnSeason = w)",
       x = "Week",
       y = "KUD95") +
  scale_y_continuous(limits = c(0, 15)) +
  theme_minimal()

grid.arrange(plotss, plota, plotw)

##########################################################################################################
week_kuds$Week <- as.numeric(week_kuds$Week)

#Model that describes KUD95 over Week by Spawning season
gam_model <- gam(KUD95 ~ s(Week, by = SpawnSeason), data = week_kuds, family = Gamma(link = "log"))
summary(gam_model)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## KUD95 ~ s(Week, by = SpawnSeason)
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.10958    0.00418   26.21   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                         edf Ref.df      F p-value    
## s(Week):SpawnSeasonA  6.476  7.311  7.661  <2e-16 ***
## s(Week):SpawnSeasonSS 7.661  8.374 13.125  <2e-16 ***
## s(Week):SpawnSeasonW  8.373  8.649 28.859  <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.0293   Deviance explained = 4.29%
## GCV = 0.21295  Scale est. = 0.44276   n = 25612
#Make predictions of the model
new_data <- data.frame(Week = rep(seq(min(week_kuds$Week), max(week_kuds$Week), length.out = 100), times = nlevels(week_kuds$SpawnSeason)),
                       SpawnSeason = factor(rep(levels(week_kuds$SpawnSeason), each = 100)))

new_data$predicted_KUD95 <- predict(gam_model, new_data, type = "response")

ggplot(new_data, aes(x = Week, y = predicted_KUD95, color = SpawnSeason)) +
  geom_line() +
  labs(title = "Predicted KUD95 over Weeks by Spawning Season",
       x = "Week",
       y = "Predicted KUD95") +
  scale_y_continuous(limits = c(0, 15)) +
  theme_minimal()

week_kuds$Week <- as.factor(week_kuds$Week)

plot(week_kuds$Week, week_kuds$KUD50)

glm_week1 <- glm(KUD50 ~ Week, data = week_kuds, family=Gamma(link="log"))
with(week_kuds, plot(KUD50 ~ Week, pch = 1, col="deepskyblue"))
seq1 <- levels(week_kuds$Week)
predictweek1 <- predict(glm_week1,newdata=data.frame(Week=seq1), type="response")
lines(seq1, predictweek1, lty=1, col="red")

glmm_spawn <- glmmTMB(KUD95 ~ Spawn + (1|Species), data = week_kuds, family = Gamma(link="log")) 
summary(glmm_spawn)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##  25460.0  25492.6 -12726.0  25452.0    25608 
## 
## Random effects:
## 
## Conditional model:
##  Groups  Name        Variance Std.Dev.
##  Species (Intercept) 0.1492   0.3862  
## Number of obs: 25612, groups:  Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.148 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 0.122603   0.071420   1.717    0.086 .  
## Spawnyes    0.065522   0.005057  12.957   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lm <- lm(KUD95 ~ Spawn * Species, data = week_kuds)